It goes without saying that artificial intelligence has digitalized everything these days, including the healthcare sector—ranging from mental health chatbots to health assessment and monitoring tools. While these tools are impressive in terms of quality and speed, many users may abandon them after initial use. Alternatively, there may be a lack of sufficient trust in user data privacy, and this inability to capture consumer trust can leave product developers disheartened. These are just a small fraction of the challenges and issues that the digital health sector faces today.

These concerns are not merely theoretical assumptions. Recent digital health industry reports show that sustaining user engagement and retention remains one of the most critical challenges in this field. Despite heavy investments in developing technical and AI capabilities, many digital health startups face high drop-off rates and declining user engagement after the first few weeks of use. Recent reports from Rock Health and Deloitte have also shown that trust, user experience, and user-perceived value are among the most critical factors determining the success or failure of digital health solutions.

Let us dissect the core challenge a bit more deeply.

The reality is that the ultimate success of digital health tools does not depend solely on their technical prowess, even though the most precise mathematical calculations are designed and implemented by elite engineering teams to build these tools. Rather, their ultimate success depends on the quality of the user’s “cognitive,” “motivational,” and “behavioral” experience. In other words, the core issue is not simply “what the AI knows” and how fast it delivers it to users; the golden nugget is “how the human interacts with the AI.”

In the previous article or part one, the importance of “learning sciences” in developing “human-centered AI” in digital health was discussed. I highlighted the crucial point that the missing link in AI technology, including digital health, is the absence of a vital foundation known as the learning sciences. The present article is an operational continuation of that discussion, attempting to demonstrate how the learning sciences can serve as a framework for designing cognitive and behavioral experiences in AI-driven digital health tools.

In this article, I offer recommendations that are more operational in nature for manufacturers and designers of AI tools within the digital health industry.

Learning Sciences as the Foundation for AI Design in Digital Health

The learning sciences and psychology of learning consist of a body of findings and theories regarding how a relatively permanent change occurs within an organism or learner. These changes depend on various internal and external factors. Furthermore, this change involves the learner’s cognitive, behavioral, motivational, and physiological dimensions.

With this simple description, it becomes clear that the learning sciences are not limited strictly to educational environments. Because they study the processes of cognition, attention, motivation, mental engagement, feedback, self-regulation, and behavior change in human-environment interaction, the learning sciences are vital wherever learning, interaction, action and reaction, or behavioral continuity are involved. They aid us in understanding the behaviors, motivations, cognitions, and perceptions of learners.

Especially in the era of AI, the science and psychology of learning demand deeper immersion and greater precision in constructing AI tools. This urgency arises from growing concerns that these tools may not be human-centered, neglecting the existential dimensions of the human being as the primary user.

One of the most important areas of AI application is digital health. The learning sciences can serve as a necessary prerequisite in AI design, acting as both an interpreter and a facilitator.

In what ways are they a prerequisite for AI and digital health?

They are a prerequisite because digital health tools can themselves be viewed as environments for learning and behavior change—environments where users are constantly interpreting information, making decisions, regulating behavior, and building trust. Many current digital health tools, despite their technical complexity, are not designed based on the cognitive, behavioral, motivational, and transactional complexities of human beings.

Some users have reported that the explanations provided by AI systems were ambiguous, complex, or even confusing to them. This finding aligns with the World Health Organization (WHO) report on the ethics and governance of artificial intelligence for health. The report emphasizes that explainability is only valuable when it is understandable to the end-user, as overly complex explanations can themselves become a factor in reducing trust and increasing confusion.

In certain studies, users have stated that they cannot comprehend the system’s decision-making logic and are forced to simply trust or distrust its output blindly. In many cases, the user eventually abandons these tools after a while—a reaction that a learner might similarly display in an AI-driven educational environment!

These and similar problems demonstrate that providing information clearly and orderly is not enough on its own. The interaction must be meaningful and comprehensible within the user’s cognitive dimension; the tool must understand the user’s behavior and reinforce their motivations. As a result, continuous, purposeful interaction and trust will be fostered. It is precisely through purposeful interaction and trust that the tool becomes useful and works in service of the consumer (or the learner).

Designing Tools Aligned with the User’s Cognitive Dimension

As previously stated, the human being is a creature of interwoven, complex dimensions. According to psychological and especially learning theories, a major part of the learning process occurs mentally within the dimension of cognition. Therefore, understanding the formula of learning and its cognitive dimension is an essential blueprint for designing digital tools.

For instance, in the human learning process, an unfamiliar and unknown topic transitions into a familiar one through distinct stages. This process can typically occur via mental stimulation and environmental support. If this learning is deep and meaningful (rather than superficial or based on rote, parrot-like memorization), it can be recalled for a long time, and the likelihood of forgetting is minimized.

The science of learning encompasses cognitive theories that emphasize concepts such as perception, meaningfulness, the integrated whole, problem-solving, scaffolding, and similar ideas. If designers implement these abstract concepts operationally, they can achieve practical results, including solving the following issues:

1. Information Overload: One of the most significant challenges in digital health tools is information overload. For example, in some studies conducted on chronic disease monitoring platforms, users reported that a high volume of simultaneous notifications, charts, and recommendations led to mental fatigue and a decreased willingness to continue usage. Researchers describe this phenomenon as a type of Cognitive Overload, which can even degrade the quality of health decision-making. Users often interact with these tools under conditions of stress, anxiety, or mental exhaustion. In such states, presenting a massive volume of data, alerts, or advice simultaneously can induce cognitive fatigue, confusion, and reduced decision-making quality.
2. Complex Explanations: In some research, users of AI-driven health systems reported that overly complex explanations did not increase their trust; instead, they heightened anxiety, hesitation, and mental strain. These findings demonstrate that successful cognitive design does not mean providing more information, but rather reducing mental strain and facilitating user comprehension.
3. Sustaining Attention: Maintaining users’ attention and mental engagement is another major challenge in digital health. Many health applications are abandoned by users after a short period. Reviews published in the mHealth sector show that a significant portion of health app users severely reduce or entirely stop their interaction within the first three months. This indicates that initially acquiring a user and retaining their cognitive engagement for continuous use are two completely different challenges. Part of this issue stems from the interaction experience becoming repetitive, impersonal, and cognitively tedious. The missing goal here is the personalization of the process!
4. Gradual Adaptation: On the other hand, altering a user’s attitude and perception is typically a gradual process, not an instantaneous one. Yet, some digital health tools deliver a large volume of recommendations and information abruptly, without accounting for the user’s gradual learning and adaptation process. Learning sciences can help design experiences that create progressive, sustainable paths for shifting attitudes and beliefs toward a process or a tool, rather than applying sudden pressure.

Designing Tools with Regard to the Users’ Behavioral Dimension

As noted earlier, learning involves permanent changes in behavioral potential. Therefore, if a change occurs in the users’ cognition and attitude, we expect to see corresponding changes in their behavioral performance as well.

The learning sciences introduce frameworks to help us understand when and how a behavior becomes consolidated. How, and under what conditions, can we successfully navigate the channel of user cognition, establish a positive attitude toward using a tool or smart test, and ultimately compel them toward stable, purposeful behavior?

The psychology of behavior, as a branch of the learning sciences, steps in at this stage to assist digital tool designers. It prescribes that the formation and continuity of behavioral learning follow specific stages. Therefore, you must clearly define the behavioral prescription you intend to instill in the user:

For instance, in many diabetes management or weight loss programs, merely presenting information about an individual’s health status has not led to behavior change. Studies have shown that when tools provide features for gradual goal-setting, self-monitoring, and continuous feedback, the probability of forming sustainable health behaviors increases.

In short, determine what the target behavior is and define it clearly. Through what stages and micro-steps does this behavior form? What types of feedback and responses guide the user toward the final target behavior? Once the target behavior is formed, what factors or feedback mechanisms can sustain it? And based on what metrics can we determine that the user’s behavior and its continuity result from the proper functioning of the tool?

Furthermore, because our objective is to build human-centered AI and tools, we do not intend to control the user. Instead, we aim to reinforce healthy attitudes and behaviors by boosting their sense of self-efficacy and perceived control over their health journey. In doing so, we guide their behavior and choices along the right path, aligned with the human blueprint.

In fact, one of the growing concerns in the literature on AI in healthcare is the reduction of Human Agency. Some experts have warned that if systems replace human decision-making rather than enhancing it, cognitive dependency and diminished independent judgment may lead to unintended consequences. Hence, the goal of human-centered design must be user empowerment, not user replacement.

Additionally, creating sustainable behavioral habits requires progressive interaction, continuous feedback, and a design that adapts to the real-world context of users’ lives. Tools designed without considering the cognitive and social conditions of the user frequently fail to yield lasting change. Understanding behavioral science and the factors influencing the reinforcement or weakening of a response helps designers correctly guide user behavior while identifying and controlling potential confounding variables inherent in digital tools.

Designing Tools Aligned with the User’s Motivational Dimension

Precisely when some neuroscience specialists argue that everything occurs at the level of cognition and that all other complex human aspects are overshadowed by it, the learning sciences (of which neuroscience is only a part) tell us it is not that simple!!! Learning an idea is a process. If we want a meaningful idea—such as using a health monitoring app—to transform into a highly repetitive, sustained behavior, we must account for other human dimensions as well!

“Trust” is one of the most critical factors in the adoption and motivational continuity of digital health tools. However, trust is not built solely through technical transparency. Users need to feel that the system is understandable, predictable, and psychologically safe.

Several studies have shown that complex or overly technical explanations fail to build trust and instead trigger greater anxiety and confusion among users. Moreover, concerns regarding privacy, data sharing, and the secondary use of health data represent major drivers of distrust among users. In digital health, trust is not merely a technical issue; it is part of the relational experience between the human and the system. For this reason, user experience design must consider psychological safety and relational trust alongside technical security.

Why is trust important? Because it is the loop that connects a user’s cognition, beliefs, and attitudes to their actual behavior! It generates the necessary motivation for follow-through, and ultimately, consolidates a behavior.

Findings from studies conducted on mental health chatbots indicate that anxiety over how personal data is stored, secondary data use, and a lack of transparency regarding data ownership are primary factors driving down user trust. In many instances, users evaluated the perceived quality of the relationship with the system as even more critical than the technical complexity of the algorithm.

While many digital health tools focus heavily on delivering information, possessing information does not automatically translate into the “motivation” required for behavior change. If the user does not feel capable of performing the recommended behavior, the likelihood of continued system utilization drops.

Alongside trust as a motivational component of user behavior, one of the most foundational concepts in the psychology of learning is “self-efficacy.”

Extensive research in health behavior change demonstrates that individuals who believe they possess the capacity to execute recommended actions are far more likely to initiate and maintain the new behavior. Consequently, successful design does not stop at giving advice; it must craft an experience where the user can taste small but meaningful victories.

Self-efficacy refers to an individual’s belief and confidence in their own abilities to organize and execute the courses of action required to achieve a specific goal. This psychological attribute can be modulated via controllable, situational feedback, and AI designers can leverage it as a key lever to impact human motivation.

In a digital health environment, this motivational characteristic can serve as the driving force behind consumer behavior when interacting with smart medical tools. Therefore, AI-driven tools must be capable of reinforcing a sense of empowerment and progressive mastery in the user, rather than merely broadcasting a barrage of alerts and directives. Studies indicate that users achieve more sustainable, satisfying engagement with health systems when feedbacks are personalized, actionable, and contextualized within the actual reality of their daily lives.

Feedback itself is only effective when it is timely, clear, and meaningful. Generic, non-actionable feedback—such as vague lifestyle advice—typically exerts a highly limited impact on behavior change. Conversely, contextualized, action-oriented feedback can significantly heighten both the cognitive and motivational engagement of the user.

Expected Operational Implications for Design and Development Teams

Many digital health tools still focus predominantly on algorithmic performance and technical functionalities, whereas the sustainability of human-system interaction relies on the quality of the cognitive and behavioral experience—and, of course, the motivational loop that links cognition to behavior and drives its continuity.

The learning sciences and psychology of learning can empower design and development teams to move far beyond the mere metrics of “ease of use” and “time management.” This shift is the most vital achievement of a cognitive, motivational, and behavioral architecture governing human-AI interaction.

This issue holds particular urgency for startups, product design teams, and digital health developers; the true success of these tools does not hinge on the sheer number of features, but on their capacity to preserve sustained engagement, secure trust, and guide human behavior change.

The learning sciences offer a framework to design tools that are not just usable, but comprehensible and justifiable within the users’ cognitive schemas. It shapes and directs user behavior under their own autonomy through self-regulation mechanisms, supplies the motivational loops connecting thought to action, and stabilizes user behavior.

Perhaps the most definitive question for the future of digital health is not how much smarter artificial intelligence will become, but rather how much better it can comprehend human cognition, motivation, agency, and behavior. Ultimately, the most successful tools will not necessarily feature the most advanced algorithms, but those that possess the deepest understanding of the human being.

References

Bandura, A. (1997). Self-Efficacy: The Exercise of Control. New York: W.H. Freeman.

Schunk, D. H. (2020). Learning Theories: An Educational Perspective (8th ed.). Pearson.

Hergenhahn, B. R., & Olson, M. H. (2015). Theories of Learning (7th ed.). Pearson.

Zimmerman, B. J. (2002). “Becoming a Self-Regulated Learner: An Overview.” Theory Into Practice, 41(2), 64-70.

Deloitte. (2024). 2024 Global Health Care Outlook.

Deloitte Center for Health Solutions. (2024). Digital Transformation and Consumer Engagement in Healthcare.

Rock Health. (2024). Digital Health Consumer Adoption Survey.

Blease, C., Kaptchuk, T. J., Bernstein, M. H., et al. (2019). “Artificial Intelligence and the Future of Primary Care.” The Lancet Digital Health, 1(8), e353-e354.

Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.

World Health Organization (WHO). (2021). Ethics and Governance of Artificial Intelligence for Health.

Sweller, J. (1988). “Cognitive Load During Problem Solving: Effects on Learning.” Cognitive Science, 12(2), 257-285.

The post Operationalizing Learning Sciences for Human-Centered AI in Digital Health appeared first on Medika Life.

At a time when Artificial Intelligence is rapidly weaving itself into the fabric of physical and mental healthcare, basic user-friendliness, processing speed, and market acceleration are no longer enough. To build digital solutions that actually work, we must grasp how humans learn, adapt, and transform their behaviors. This is exactly where the learning sciences become vital. Put simply, until we decode the mechanisms of ‘deep learning in humans’ through the lens of learning sciences, the concept of ‘Deep Learning’ in AI development will never reach its true potential.

Digital health, much like any other modern domain, is now permanently tied to technology. From education and corporate structures to parenting and economics, technology is built to streamline processes, widen access, and boost precision. At its core, technology was created to serve humanity across individual and social spheres, and digital health stands as one of the most critical testing grounds for this promise.

Yet, alongside this reality lies a much bigger issue—one that is gaining traction and deserves a rigorous, interdisciplinary look.

The question isn’t whether technology is inherently good or bad; it is that even the most advanced technology remains ineffective if it fails to align with human blueprints.

Today, more than ever, we need to look at AI and digital systems through a deeply human lens. This means moving away from treating an individual merely as a ‘user,’ a ‘data processor,’ or a passive ‘receiver,’ and instead recognizing them as a multi-dimensional, complex, living being.

In digital health, our core focus is the human being—the patient striving for recovery, the client seeking a precise diagnosis, the therapist requiring sharper diagnostic tools, or the physician leaning on technology to make high-stakes clinical decisions. The human is always the ultimate destination. If a digital tool is to succeed in this space, it must genuinely connect with real people, accounting for their cognitive, behavioral, biological, and experiential complexities.

Why Research in the Learning Sciences is Indispensable

In the digital health space, the real challenge is never just about getting someone to install an app or use a digital tool temporarily. The true measure of success is whether that tool can drive a real, lasting change in human behavior, attitude, and lifestyle. If a person engages with a platform for a brief period but experiences no sustainable shift in their health or daily habits, the technology has fundamentally missed its mark.

This is where the learning sciences help us elevate technology design far beyond surface-level mechanics and computational algorithms. When we understand how a person actually internalizes information, we can build better communication strategies, deliver more constructive feedback, apply the right behavioral reinforcements, and create environments that foster genuine trust, motivation, and user engagement.

Furthermore, this scientific backing allows us to grasp privacy and data security from the psychological standpoint of the user, since a patient’s willingness to trust a system is directly tied to how safe they feel sharing their data.

Two Foundational Pillars: Trust and Continuance Intention

To see how the learning sciences practically guide human behavior in the era of AI, we can look at two crucial dynamics in digital health:

1. The Mechanics of Trust
Trust is the ultimate currency in digital health, because users are asked to hand over highly sensitive personal, biological, and psychological data to an algorithm.

2. Continuance Intention and Habit Formation
Capturing a user’s attention at launch is relatively easy; keeping them engaged over time is where the tech industry routinely struggles.

Conclusion

The defining critique of modern AI is not its widespread adoption, but its lack of authentic human-centricity. Successful digitalization in healthcare cannot rely solely on technical scalability; it must place the complex human being squarely at the center of the design process.

Technology only gains meaning when it can understand human beings, build a relationship with them, earn their trust, and guide them toward lasting well-being.

Ultimately, the future of digital health will not be measured by raw processing power, but by the depth of the developer’s understanding of the human condition.

References

Sucala, M., Cole-Lewis, H., Arigo, D., Oser, M., Goldstein, S., Hekler, E. B., & Diefenbach, M. A. (2021). Behavior science in the evolving world of digital health: Considerations on anticipated opportunities and challenges. Translational Behavioral Medicine, 11(2), 495–503. https://doi.org/10.1093/tbm/ibaa034

Bai, B., & Guo, Z. (2022). Understanding users’ continuance usage behavior towards digital health information system driven by the digital revolution under COVID-19 context: An extended UTAUT model. Psychology Research and Behavior Management, 15, 2831–2842. https://doi.org/10.2147/PRBM.S364275

The post Human-Centered AI in Digital Health: Why Learning Sciences Matter appeared first on Medika Life.

A bill passed by the state legislature in March then signed into law by Democratic Gov. Jared Polis allows Colorado to further uncouple itself from federal guidance.

The law allows health officials to follow the recommendations of national medical groups when making decisions such as purchasing bulk vaccines for the Medicaid program.

“We are insulating our state from the dysfunction coming out of Washington,” said Democratic state Sen. Kyle Mullica, a co-sponsor of the bill and a registered nurse. “We’re going to rely on science.”

“From fighting during the pandemic for Coloradans to get vaccines as quickly as possible to combating the Trump Administration’s barriers to getting vaccinated, we have expanded access to vaccines for Coloradans who want them,” Polis said in a statement when he signed the law.

Colorado is one of at least 29 states that, along with Washington, D.C., have taken steps to bypass the new federal recommendations amid worries that the changes could chip away at public trust in vaccines and erode broad vaccine coverage.

Previously, Colorado, like most states, had followed federal guidance set by the Centers for Disease Control and Prevention. In January, CDC advisory panelists, selected by Health and Human Services Secretary Robert F. Kennedy Jr., removed six pediatric immunizations from the agency’s universal recommendation list.

Last year, doctors, scientists, local leaders, and other supporters came together to form an outreach and advocacy coalition called Colorado Chooses Vaccines.

The group aims to offer a clear, unified voice on the proven benefits of vaccines and reassure residents confused by the many federal changes.

Carol Boigon, a former Denver City Council member, joined the group because she wants more people to hear her own chilling story about vaccine-preventable illness.

“Every summer everybody got sick,” Boigon said, recounting her childhood in 1950s Detroit.

The illness was polio, a highly contagious viral disease that attacks the nervous system, sometimes causing partial or full paralysis.

During the summer of 1953, “the whole block was sick and some of us got crippled, and that was just the way it was,” she said.

New Group Steps Up

Boigon’s personal history will be part of the coalition’s work to educate new generations about the dangers of infectious diseases that were once common in the U.S. but are now relatively rare.

The group, which formed last September, will also compile vaccine information from medical groups and the state health department and advocate for policy proposals with the state government.

“It was in direct response to the federal threats,” said another coalition member, former state lawmaker Susan Lontine. She leads the nonprofit Immunize Colorado.

Another member, public relations specialist Elizabet Garcia, wants more outreach to Hispanics, whose vaccination rates lag behind other groups’.

“A lot of time it’s this fear that they’re going to have to pay out-of-pocket, that their insurance doesn’t cover it, that they might not even have insurance in general,” Garcia said.

Boigon was 5 when she got sick and was hospitalized for six weeks with a fever. The virus attacked her spine.

“None of my limbs worked immediately afterwards,” Boigon said.

Although she regained function in her other limbs, her right arm never fully recovered. She had to adapt, relearning everyday tasks such as reaching out to shake hands with people with her left hand.

In 1955, not long after she got sick, the new polio vaccine became more widely available to the public. As vaccinations took off, U.S. cases of polio, once one of the nation’s most feared diseases, dropped by an estimated 85%-90%.

Increasing Public Trust

State leaders have taken other steps to promote public health. After the Trump administration pulled the U.S. out of the World Health Organization, several states, including Colorado, decided to join the WHO’s Global Outbreak Alert and Response Network on their own.

Colorado also joined a multistate lawsuit challenging the Trump administration’s changes to the childhood vaccine schedule.

And the new state law has provisions besides allowing the state to diverge from federal recommendations. It codifies pharmacists’ ability to prescribe and give vaccines themselves. It also increases legal protections for healthcare workers who give vaccines.

“This law will provide more clarity to guide all Coloradans, including providers who administer vaccines,” Lontine said.

But the legislation has opponents who say it would interfere with parental choice and claim vaccines might be unsafe or ineffective.

“I just want to make sure we’re not just getting into a big political dispute between the federal recommendations — the CDC and so forth — and different political views in Colorado here,” said Republican state Sen. John Carson, who voted against the vaccine bill.

NPR contacted the U.S. Department of Health and Human Services about Colorado’s new law. Spokesperson Emily Hilliard answered in an email: “The updated CDC childhood schedule continues to protect children against serious diseases.”

Preventable Illnesses Surge

The flurry of statewide activity comes as Colorado and the nation have seen surges in illnesses such as flu and measles.

As of mid-May, Colorado had recorded 22 measles cases this year. In 2025, it registered 36 cases, according to the state health department, far surpassing totals from previous years.

Across Colorado, kindergarten vaccination rates for measles were 88% last school year — with only a few counties achieving rates of 95%, the level needed for herd immunity, according to data published by The Washington Post in December.

This has also been Colorado’s worst flu season in recent years.

Vaccination rates for both flu and covid-19 have dropped slightly in Colorado, according to the state health department.

Eight children in Colorado have died this season from flu; one from covid; and one from RSV, or respiratory syncytial virus. Vaccines for all three are available for children and recommended by the state’s health department.

Kennedy, a longtime anti-vaccine activist, has defended his decisions to overhaul the recommended schedule for childhood vaccinations.

In March, a federal judge put on hold many of the changes.

“We’re not taking vaccines away from anybody. If you want to get the vaccine, you could get it. It’s going to be fully covered by insurance just like it was before,” Kennedy told CBS News in January.

When a reporter suggested the new changes could result in fewer people getting a flu vaccine, Kennedy said: “Well, that may be, and maybe that’s a better thing.”

Boigon is sometimes incredulous at everything that has happened.

“It’s like we’re going backwards,” she said. “It’s like we have decided we don’t want a modern life; we want to be back in the 1950s, where children are sick and dying.”

This article is from a partnership with Colorado Public Radio and NPR.

The post Colorado Charts Its Own Course on Vaccines Amid Federal Pullback appeared first on Medika Life.

Despite this extraordinary pace of innovation, something fundamental remains broken. Patients still struggle to navigate care. Physicians continue to wrestle with fragmented systems, administrative overload and technologies that often add work rather than reduce it. Health innovators repeatedly introduce sophisticated tools into environments overwhelmed by operational complexity, lack of governance, cybersecurity concerns, workflow disruption and communication gaps.

The issue is no longer whether innovation benefits care. The issue is friction.

Consumers compare health care experiences to every interaction in daily life. They compare health care to Apple, where design simplifies complexity, to Amazon, where communication is continuous and immediate, and to banking and travel platforms providing real-time updates and seamless transactions. Some may even compare it to Domino’s Pizza, which promises delivery within 15 minutes or the pie is free. Expectations surrounding responsiveness and convenience have fundamentally changed.

Then they enter health care environments where forms are repeated, portals fail to communicate, prior authorizations delay treatment and updates disappear into silence. Patients are left to navigate disconnected systems during moments of vulnerability. The expectation gap between consumer and health care experiences continues to widen and increasingly shapes reputation.

In Healing the Sick Care System: Why People Matter, the observation is made that “Health care isn’t failing because we lack innovation. It’s failing because the system around that innovation has calcified.” The statement remains painfully real because innovation alone does not create confidence. Experience does.

Patients Remember the Journey, Not the Molecule

The patient and physician experience is shaped less by what a product promises and more by what happens after that promise enters real life. A medicine may be clinically meaningful, yet the experience surrounding it can still become exhausting if coverage is difficult to secure, prior authorization is confounding, specialty pharmacy coordination is slow, follow-up instructions are unclear or support programs require patients to become navigators of their own care.

In those moments, people are not judging science on its own merits. They are judging the total experience of trying to make that medicine or care available and understandable.

Physicians face their own administrative version of friction. A therapy may be medically appropriate, yet before treatment can begin, office staff must determine coverage, complete documentation, respond to payer step-through requirements, manage rejection appeals and explain delays that were never created in the exam room. Every additional administrative step consumes time, stretches staff and places additional strain on the physician-patient relationship. Even non-medical formulary changes can force physicians to restart conversations, explain unexpected medication switches and reestablish patient confidence in treatment decisions already made.

Patients remember counting the hours as they waited for answers. Physicians remember losing uncompensated time navigating systems and approvals. Nurses remember caring for patients through computer screens while typing notes into laptops on rolling carts in crowded hallways. Office managers remember the relentless cycle of paperwork, rejected claims, disconnected portals and endless callbacks trying to move care forward.

The therapy may eventually do its job, yet the pathway becomes inseparable from the memory associated with the brand, the company and the broader health care system. Every new process, technology and treatment promises improvement. For patients and health professionals, however, if the path to care feels uphill, the friction surrounding the experience can overshadow the value of the benefit.

For many patients, repeated uncertainty, delays and administrative obstacles contribute to a form of medical PTSD, where anxiety surrounding the system becomes inseparable from the treatment experience. For health professionals, the constant burden of navigating fragmented systems, managing approvals and compensating for communication gaps has become a leading contributor to burnout.

Friction is rarely remembered as an operational issue inside organizations. Patients and physicians experience it personally. This is why communication must be elevated operationally within health care. Communication is not marketing layered onto innovation after development is complete.

Health care organizations often think they are going through the process of delivering a product, therapy or platform. Patients and physicians experience something more personal: time invested in every interaction surrounding the innovation is time lost forever.

Health Technology Cannot Create More Work

The same reality applies to health technology startups and digital health innovators. Technological advancement alone does not guarantee adoption within health care environments already burdened by operational complexity and workforce fatigue.

Health care organizations do not merely evaluate whether technology works. They evaluate whether it integrates with existing workflows, whether cybersecurity standards are state-of-the-art, whether onboarding is manageable, whether interoperability gaps create additional burdens, and whether the institution can trust the accuracy of data.

Every additional step is a friction point, while every unresolved operational issue becomes part of the patient and physician experience surrounding the journey.

A sophisticated AI platform that requires clinicians to validate outputs continuously adds cognitive burden. A monitoring platform generating clinically important alerts contributes to fatigue. A system that requires extensive retraining or manual workarounds may succeed in demonstration but stumble in real-world conditions.

Innovation may arrive elegantly designed; however, it enters health care environments already strained by workflow complexity, disconnected systems, cybersecurity demands and administrative fatigue. The operational realities surrounding implementation often become as important as the innovation itself.

That reality does not diminish the importance of continuous invention. It reinforces the importance of implementation, communication and operational design within real-world clinical environments.

This shift is increasingly visible across the global health innovation marketplace itself. At HLTH Europe 2026, conversations are moving well beyond excitement surrounding artificial intelligence, digital therapeutics and next-generation platforms. The agenda sessions focus on interoperability, workflow integration, governance, patient engagement and operational implementation. Conference themes repeatedly emphasize connected systems, coordinated experiences and technologies that reduce fragmentation rather than add to a growing list of patches.

One of the more revealing themes from HLTH Europe focuses directly on interoperability and the longstanding frustration surrounding disconnected systems. The conference site notes that clinicians continue spending enormous energy managing platforms that fail to communicate effectively with one another. At the same time, artificial intelligence is increasingly viewed not as a replacement for care, but as a bridge helping systems “finally speak the same language.”

Another major focus involves provider realities. HLTH Europe speakers highlight workforce fatigue, cyber risks, operational strain and workflow challenges facing clinicians and health systems across Europe and beyond. These agenda themes reinforce a growing recognition throughout the industry that innovation cannot succeed if it increases the burden for the people expected to use it every day.

Health professionals increasingly describe a workplace dominated by more screens, more alerts, more documentation and less time with patients. Technology interrupting workflow rather than integrating into it creates resistance, regardless of how advanced the platform may appear. The hidden work behind implementation often becomes the defining experience for the people expected to use the system every day.

Cybersecurity provides another important example. Health professionals and patients may never fully understand the technical architecture protecting health information, yet they absolutely understand the emotional consequence of uncertainty surrounding data privacy, reliability and trust. Confidence in health technology is not built solely through functionality. It is reinforced through consistency, service, transparency and confidence that information is accurate, protected and responsibly governed.

Communication plays an equally important role here. If clinicians are left uncertain about updates, system changes or data governance responsibilities, confidence weakens. If patients do not understand how information is protected, trust erodes, regardless of how advanced the technology.

Communication remains inseparable from the care experience.

The organizations most likely to lead the future of health care will not distinguish themselves solely through technological achievement. They will reduce friction around the user interface, workflows and data accuracy.

The Companies That Win Will Simplify Complexity

This reality explains why access organizations such as Hims & Hers Health and Cost Plus Drugs deserve careful study from across the health care sector, regardless of whether industry leaders agree with every aspect of their business models. These organizations are built around reducing friction in how people access and experience care.

Their importance extends beyond convenience or pricing. These companies recognize that many traditional health institutions have underestimated: people increasingly expect health care experiences to reduce anxiety, simplify decision-making and provide continuity throughout the care journey.  They are “Amazon-like,” offering a “Buy It Now” simple click medical oversight option.

The rise of concierge medicine, direct-to-consumer health platforms and walk-in clinics with reduced wait times reflects a broader market signal the health sector cannot ignore. Patients are increasingly gravitating toward experiences where communication is clearer and access is more immediate.

For those able to afford concierge care, the attraction often extends beyond physician access itself. Patients value responsiveness, shorter wait times, easier scheduling, follow-up communication and the sense that someone is helping coordinate their journey through the system. Walk-in clinics and urgent care centers appeal for similar reasons. People are searching for environments where care is readily accessible, understandable and administratively manageable. The downside of loss of care continuity is offset by immediacy, which is what the consumer values most.

This migration reflects frustration with friction embedded throughout the trending health care experience. Long hold times, delayed callbacks, countless portals, disconnected records, repeated paperwork on clipboards and uncertainty surrounding next steps all shape how people perceive quality of care.

Communication once again sits at the center of the experience. Patients rarely separate operational snafus from expert care. They experience the entire journey as one connected reality – positive or negative.

The lesson is not that health care should behave exactly like retail commerce. Medicine carries ethical, scientific and regulatory responsibilities far beyond consumer transactions. Nevertheless, the operational expectations consumers now bring into the setting have changed.

People increasingly expect health care to be as responsive as the communication they experience elsewhere in life. Is that expectation reasonable?

The pharmaceutical industry, payers, providers, and health technology innovators must recognize that they no longer own just the patents on therapies, platforms or services. They also own the surrounding user experience.

Patients experience health as a continuous journey, not a “build your own adventure” exercise in navigating fragmented systems. Most people enter the system anxious and seeking reassurance from their health professionals. A delayed approval, clinically sterile information delivered through a diagnostic portal or a physician struggling to navigate complexity alongside them deepens that burden. These experiences shape how health care is remembered more powerfully than advertising campaigns or corporate positioning statements.

Those experiences ultimately shape reputations.

The future winners in health care will not simply develop innovative products. They will reduce friction around the human experience surrounding those products. They will recognize that communication, workflow design and responsiveness are not secondary considerations attached to innovation. They are part of the experience.

Patients and physicians rarely remember the elegance of molecular or system architecture behind a therapy or platform. They remember whether the experience made care delivery easier and more humane during moments that mattered.

The post Health Innovation Has a Friction Problem appeared first on Medika Life.

Health versus wellness and the loss of muscle

Health and wellness are often conflated, but they’re actually quite different. Health is the absence of disease, such as heart disease, cancer, diabetes, obesity, or dementia. Wellness is the ability to do the things in life that matter, that give joy, such as carry the groceries, open a pickle jar, cuddle your great-grandchild, or, importantly, get up off the floor without assistance.

The fundamental issue is the loss of muscle mass and the resulting decline in strength. As we lose muscle mass, we often lose our sense of balance. Muscle loss begins in midlife and initially declines at about 1% per year. It speeds up in the late 70s and early 80s, so that by age 80 or so, the average individual has lost about 40% or more of their muscle mass and muscle strength.

Physicians call this loss of muscle sarcopenia. The graphic shows a cross-section of a muscle from a man at age 25 and from a man at age 63. The younger man’s muscle is pretty much all muscle, with just a little fat around it, but the older man has thick fat around and within his much-reduced muscle.

Swedish study results are striking

There’s a study from Sweden in the Journal of Cachexia, Sarcopenia and Muscle from November 2025 called “The rise and fall of physical capacity in a general population: a 47-year longitudinal study. “

Dr. Maria Weserstahl and colleagues have followed 427 men and women for 47 years. Each participant was age 16 at initiation of the study and is now 63. Multiple tests have been conducted every few years to assess muscle strength, power, and aerobic capacity. They observed that each of these measures increased from age 16 to about age 35, then began to decline. Initially, the decline was about 0.3% per year, but it accelerated to 2.5% per year by age 63. The total decline from the peak ranged from 30% to 48%.

The first graphic shows bench press as a measure of strength. Strength is largely dependent on slow-twitch, or red, muscle fibers. The upper part of the graph shows the men, and the lower part shows the women. The key point is that, at age 16, boys were stronger than girls, and this persisted throughout adulthood. Both men and women reached peak strength about age 36 and then began the steady decline. Not appreciated from the graphic, those of either sex who had the highest level of strength at age 16 tended to have the highest strength at age 63. Those who began to exercise at any point during the 47 years had a higher or greater strength by age 63

The second graph shows vertical jump performance as a measure of power, or the status of white or fast-twitch muscle fibers. The investigators had each individual stand next to a wall and jump as high as they could, touching their fingers at the high point. Here again, the boys’ power was greater than the girls’. The peak occurred around age 26 in the men and didn’t increase in most of the women. Thereafter, both men’s and women’s power declined steadily through age 63.

The third graphic shows aerobic capacity as a measure of endurance. Here again, the boys had a higher aerobic capacity than the girls at age 16. But both men’s and women’s aerobic capacity increased substantially, peaking around age 35 and then dropping off quickly.

As noted, those at the highest point at age 16 remained at the highest level at age 63. But those who started exercising at any age reached a higher level by age 63. The message I take away from this is “it is never too late” to improve your lifestyle.

It will be very interesting to learn what happens to these muscle values as the study continues into the participants’ 70s, 80s, and 90s. But from other studies we know that muscle mass and strength continue to decline, accelerating in the late 70s and early 80s, consistent with my friend’s and my own experience.

The good news, not emphasized in this report, was that beginning to exercise led to greater strength, power, and aerobic capacity at age 63 than among those who did not.

Reclaiming lost muscle

It’s no surprise that older people will often say something like, “I don’t like it, but losing muscle is just what happens when you get older.” Or “It’s too late for me to do anything about it “. But these statements are not true! It is never too late. You can slow the decline. You can rebuild muscle. But, to be honest, it takes real work and persistence over time. The following image is of a 70-year-old triathlete. You don’t need to be that aggressive, but the point is that it is possible to maintain muscle mass, and it is possible to regain it if you have lost it.

To reverse muscle loss, the critical steps are to 1) do resistance exercises, 2) increase protein intake, and 3) increase the micronutrients found in fruits and veggies, while 4) managing stress, and 5) enhancing sleep.

Resistance exercises can be done all at once over an hour or two, and repeated later in the week. But it is often more practical to do some each day. For example, do upper-body resistance exercises on Monday and Thursday, core on Tuesday and Friday, lower body on Wednesday and Saturday, and leave Sunday as a day off.

There is a pernicious myth that seniors should not do resistance exercises because they can hurt themselves. This advice has come from well-meaning physicians, physical therapists, personal trainers, and friends, but the data is clear: resistance exercises are not dangerous and, indeed, are essential. They’re proven to be safe and effective at any age, up to and beyond age 100. Inactivity risk is far greater than weight training risk for older adults. That said, it would be wise for an older person to work with a personal trainer to develop an effective yet safe exercise plan.

It takes more effort for an older person to build muscle mass and strength than for a younger person. But there’s no question that an older person can do both, do it effectively, and do it safely. It is never too late.

Building muscle requires protein and various micronutrients. Most people, and certainly most older people, do not get adequate protein. The current rule of thumb is that an active senior should get between 0.5 and 0.9 g per pound. This translates to about 115 g of protein per day for a 150-pound active senior. It’s quite possible to consume 115 g of protein in a day, but for some, that may be daunting without adding a protein supplement. If you do choose to supplement, choose one without added sugars. Also, consider adding a creatine supplement.

Micronutrients are found in high concentration in dark, green, leafy vegetables, nuts, seeds, beans, and fruits, especially berries.

At the same time, avoid ultraprocessed foods (UPFs), reduce calories, and avoid sugar (candy, ice cream) and foods made with white flour (white bread, pasta, pastries). The reason to avoid white flour is that it quickly breaks down into glucose (sugar). Since older people are less active and have less muscle mass, they need fewer calories. This may sound like a paradox, but reducing ultraprocessed foods with their sugar, fat, and salt while increasing protein means fewer calories.

Finally, your body needs a good night’s sleep, usually 7 ½ hours and it is important to manage your chronic stresses to reduce the stress chemicals that can slow muscle regrowth.

It is never too late
Maintaining muscle mass and strength is not just about feeling well and doing those things that are both fun and important to daily living. Low muscle mass is a predictor of all-cause mortality and of lifespan. It prevents you from becoming fail which is itself a mortality predictor. Muscle mass is also key to avoiding metabolic diseases such as diabetes.

You can reverse the loss of muscle that comes with aging. You can slow muscle loss of aging. You can rebuild muscle mass and strength. And in doing so, you can remain or become “well.”

Remember that it’s never too late to make the change and modify your lifestyles for the better. It can make life more enjoyable, healthier, and add to your health span and your lifespan. Here is a plus: you will find new friends where you exercise.

Please note: This article is for your education; it is not medical advice. I recommend you take this article to your physician and seek their advice on what is best for you.

The post You Lose 40% of Muscle Strength by Your 60s and Then It Accelerates appeared first on Medika Life.

Pets can get sunburned. They can develop skin cancer. And the veterinary world is rapidly catching up to that fact with a new generation of products, prescription treatments, and protective gear that’s turning animal sun care into a serious and fast-growing industry. We can’t do what our grandparents did: just let the animals be animals, bring them indoors at night, and put them back out in the morning. We have more responsibility for them now, and we need to own up to it to protect their health.

More Than Just a Hot Day Problem

When we think about protecting our animals in summer, heat stroke usually comes to mind first. That’s important, no question. But sunburn is its own problem, and it gets a lot less attention. I know I never thought any of our dogs would be at risk of sunburn, and I am sure most of you haven’t given it much thought either.

Medically, sunburn in animals is called solar dermatitis. It’s caused by ultraviolet radiation, the same UV rays that damage human skin. UV radiation comes in two main forms. UVA causes skin aging and is linked to cancer. UVB causes the red, painful burning we associate with a bad day at the beach. Animals are exposed to both.

Fur does provide some protection. But it doesn’t cover everything, and it doesn’t help every animal equally. Pets with light-colored or white fur, thin coats, pink skin, or no hair at all are at the highest risk. That includes Dalmatians, bull terriers, white cats, and hairless breeds like the Sphynx cat. But it also includes your regular everyday golden retriever who loves to roll on his back in the sun, exposing his belly, his groin, and the underside of his tail.

The areas most likely to burn are the ones you’d expect: the nose, the ear tips, the eyelids, and the belly. Cats with white ears are especially vulnerable. Board-certified veterinary dermatologist Dr. Jill Abraham has noted that squamous cell carcinomas show up on the ear tips of cats, on their noses, and even on their eyelids. These aren’t minor skin irritations. They’re cancers.

What Sunburn Looks Like in a Pet

Early sunburn in animals shows up as reddened, warm, or sensitive skin. You might notice peeling, flaking, or your pet flinching when you touch a certain spot. A more serious burn can blister and scab. And if a pet is burned repeatedly over time, the risk of skin cancer goes up significantly.
Those cancers can include squamous cell carcinoma, hemangiosarcoma, and malignant melanoma. Early detection matters a great deal. A veterinarian can examine unusual spots, non-healing sores, or skin that looks crusty and discolored. Catching skin cancer early gives a pet a much better chance at a good outcome.

Something else worth knowing: even indoor cats aren’t completely safe. If your cat sunbathes next to a sunny window, it’s still soaking up UVA rays right through the glass.

A Growing Industry Responds

The veterinary dermatology market is booming. In 2024, the global market for veterinary dermatology drugs was valued between $10.8 billion and $17.4 billion, depending on the research firm, with projections showing it reaching as high as $29.5 billion by 2030. That kind of growth reflects how seriously the field is now taking skin health in animals.

The prescription segment leads that market. Veterinarians are prescribing more dermatological medications than ever before, including treatments for conditions worsened by sun exposure. In September 2024, the FDA approved Zenrelia, a new drug for the control of itching connected to allergic dermatitis in dogs. And in 2024, Zoetis launched a new dermatological line targeting allergic dermatitis, using bioactive ingredients backed by clinical trials.

Beyond prescription drugs, the over-the-counter segment is growing fast too, at a projected rate of about 9.7% annually through 2030. In North America alone, the pet skin care products market was valued at $2.1 billion in 2024. Protective accessories are a growing part of that picture.

UV-blocking shirts and bodysuits for dogs, with UPF 50+ ratings, are now widely available alongside hats, visors, and sun goggles specifically designed for pets. Published research in the journal Veterinary Dermatology examined UV protection for free-range pigs and found that proper shade cloth materials could meaningfully reduce harmful UV exposure in animals with light-colored skin.

The Sunscreen Problem

Here’s where things get complicated, and where it’s really important to pay attention. You cannot just grab your own sunscreen and rub it on your dog or cat. Some ingredients in human sunscreens are toxic to pets.

The two biggest concerns are zinc oxide and salicylates. The ASPCA lists both as the main ingredients to avoid in any product applied to a pet. Zinc oxide, that thick white cream you see on lifeguards’ noses, can damage a pet’s red blood cells if it’s ingested, and pets will lick it off. Salicylates, chemicals related to aspirin, can cause stomach ulcers, vomiting, and, in serious cases, liver damage if a pet eats them in quantity.

A published case report in the Open Veterinary Journal documented a small dog who developed severe anemia after repeatedly licking a zinc oxide cream that had been applied to her skin. The dog required emergency care and intensive treatment. The ASPCA Animal Poison Control Center fields many calls about sunscreen ingestion during warmer months.

The direction from veterinary toxicologists is clear: only use sunscreens specifically made for pets, and check with your vet before trying any new product. When choosing a pet-safe sunscreen, look for one that protects against both UVA and UVB rays and has an SPF of at least 30. Apply it 10 to 15 minutes before sun exposure, and reapply often, especially if your pet swims. Focus on the vulnerable spots: the nose, ear tips, belly, and any area with thin fur or pink skin.

Simple Steps That Make a Real Difference

The easiest protection is shade. Keep outdoor pets shaded during the hottest and sunniest part of the day, roughly between 10 a.m. and 4 p.m. Walk your dog early in the morning or in the evening. Provide shelter and shady spots for any animals that spend time outside.

For pets who love to bask in the sun, UV-protective clothing is a practical option. These garments have come a long way from the novelty dog shirt. They’re designed to allow comfortable movement, wick moisture, and block UV rays all at the same time.

And check your pet’s skin regularly. Run your hands over their body every few weeks. Look for new lumps, sores that won’t heal, or skin that looks unusual. The earlier you catch something, the better.

Your pet trusts you completely. They don’t know what UV radiation is. They don’t know that the warm patch of sunlight on the floor could be doing them harm. That part is yours to handle. And now, thankfully, the veterinary industry is giving you more tools than ever to do it.

The post Your Pet Can’t Put On Sunscreen, But They Need It, Anyway appeared first on Medika Life.

This white paper was researched and written with the assistance of Claude Sonnet, an AI system developed by Anthropic. AI assistance was used to accelerate literature retrieval, improve the quality of writing, and support editing and formatting. The intellectual framework, argument structure, source selection, and all substantive claims reflect the author’s own thinking and direction. All citations have been identified and verified by the author. The author assumes full responsibility for the accuracy and integrity of all content presented in this paper.

Executive Summary

Artificial intelligence is arriving in American healthcare at scale. Health systems are investing in AI-powered diagnostics, clinical decision support, predictive analytics, and administrative automation. The promise is real. So is the risk. Machine learning models learn from data. In healthcare, that data is generated by the systems deploying the AI. And if those organizations have not been designed to produce clean, reliable, clinically meaningful data, then the AI built on top of them will automate and amplify the dysfunction already present in the system, not correct it.

This is the argument this paper makes. It is not primarily an argument about technology. It is an argument about organizational design.

The concept of the Learning Health System, formally defined by the Institute of Medicine in 2007, describes a system in which knowledge generation is so deeply embedded in the delivery of care that improvement becomes continuous and self-reinforcing rather than episodic and externally driven. Nearly two decades after that definition was published, widespread adoption remains limited. The gap is not one of awareness. It is one of operationalization. And in an era of AI-driven healthcare, the cost of that gap is no longer just missed improvement opportunities. It is corrupted training data, biased models, and clinical decisions shaped by intelligence that learned the wrong things from a system that was never designed to learn at all.

This paper examines why the Learning Health System has not been built at scale, using the organizational thinking design framework of Vision, Mission, Capacity, and Learning developed by Drs. Derek and Laura Cabrera, and the wicked problem literature in strategic management. It identifies three conditions most visible in clinical, policy, and public discourse as illustrations of the organizational design problem: physician burnout, electronic health record burden, and payer interference through prior authorization. These three are not presented as an exhaustive explanation. They are presented as a coherent causal chain that leads directly to the data quality crisis sitting underneath every AI deployment in American healthcare today.

The paper concludes not with a prescriptive framework but with an invitation to think differently about how health systems are designed, led, and held accountable, before the next wave of AI investment locks in the mistakes of the current one.

I: A Conversation That Sparked a Question

American healthcare is in the middle of an AI gold rush. Health systems, technology companies, and investors are moving fast, betting that machine learning, predictive analytics, and AI-powered clinical tools will transform how care is delivered and how outcomes are measured. The enthusiasm is understandable. The technology is genuinely powerful. But a question is not being asked loudly enough: what kind of system is this AI learning from?

In early 2026, Gil Bashe, Chair of Global Health and Purpose at FINN Partners, published Healing the Sick Care System: Why People Matter, arguing that American healthcare is not failing because it lacks innovation, investment, or talented people.[2] It is failing because it has lost sight of the people it exists to serve. That argument sparked a different but related question for the author: what kind of system do we actually have?

We call them healthcare systems. We build teaching hospitals. We invest in teaching rounds and residency programs and the careful, structured transmission of clinical knowledge from one generation to the next. Teaching is a word we use with confidence and pride in medicine. But when do we talk about the system itself learning? Not individuals acquiring competency, but the institution changing what it does based on what it discovers. Teaching and learning are not the same thing, and that distinction, hiding in plain sight, may be one of the most consequential unexplored ideas in American healthcare today, especially at a moment when AI is being asked to learn from systems that were never designed to learn themselves.

This question led to an examination of a concept that has existed in formal academic and policy literature since 2007 but has not entered the broader conversation about healthcare reform in any meaningful way: the Learning Health System.

II: What Is a Learning Health System, and Why Has It Not Been Built?

Understanding why AI in healthcare is sitting on a compromised foundation requires understanding what a Learning Health System actually is, and why one has never been fully built. The Learning Health System is not simply a framework for improving data quality. It is the only organizational model in which clean, clinically meaningful data is a natural and continuous byproduct of how care is delivered. Every other approach to the data quality problem in healthcare AI is essentially trying to fix the output without changing the system that produces it. The Learning Health System changes the system. That is why it matters now, and that is why AI in healthcare makes it urgent.

The term Learning Health System entered the formal vocabulary of American medicine in 2007 when the Institute of Medicine convened a roundtable on value and science-driven health care. The definition it produced has held up well: a Learning Health System is one in which knowledge generation is so embedded into the core of the practice of medicine that it is a natural outgrowth and product of the healthcare delivery process and leads to continual improvement in care.[1] Knowledge generation in this vision is not adjacent to practice. It is not a research department down the hall or a quality improvement initiative launched when funding permits. It is embedded in practice itself, and it leads to continual, self-reinforcing improvement in which care creates evidence and evidence improves care.

Nearly two decades later, widespread adoption remains limited. Not because the concept has been ignored. It has attracted sustained attention from the National Academy of Medicine, federal agencies including Agency for Healthcare Research and Quality (AHRQ) and Patient-Centered Outcomes Research Institute (PCORI), major academic health centers, and research networks such as National Patient-Centered Clinical Research Network (PCORnet) and the NIH’s National COVID Cohort Collaborative. What has proven difficult is operationalization at scale: figuring out what a genuine commitment to learning actually means in terms of changed practice, realigned infrastructure, new staffing, revised policy, and real shifts in organizational culture. The IOM’s deliberately broad definition, intended to maximize applicability, had an unintended consequence. It left every institution to solve the operationalization problem largely on its own, without a shared language for the organizational design work that learning at scale actually requires.[16]

The cycle the Learning Health System literature describes is straightforward in concept. Knowledge is identified and synthesized to address clinical challenges through evidence reviews and clinical practice guidelines. That knowledge gets applied in care delivery through clinical decision support and care pathways. Care delivery generates data, captured in patient registries and EHRs, assessed for performance, and fed back into the knowledge generation process. The loop closes. Patients are at the center throughout, not as passive recipients of decisions made elsewhere, but as active contributors to the knowledge the system generates.[11]

It is also worth being clear about what a Learning Health System is not. It is not a teaching hospital. A teaching hospital organizes itself to transfer knowledge from experienced clinicians to trainees. Knowledge flows in one direction, and the institution learns incidentally if at all. A Learning Health System organizes itself to change based on what it discovers in the course of delivering care. The institution itself is the learner. American medicine has invested heavily in building teaching capacity. The investment in learning capacity, the organizational infrastructure that allows a health system to discover, synthesize, and act on what its own practice is telling it, has been far more limited and far less systematic.

The concept operates at two levels that are easy to conflate. At the macro level, it describes what American healthcare as a sector could become. At the micro level, it is an organizational design challenge that has to be solved institution by institution through specific decisions about how care is delivered, how data is captured, how knowledge is synthesized, and how evidence actually changes what clinicians do on any given day. The macro vision only becomes real through micro organizational choices. The research literature suggests those choices have not yet been made in ways that support learning at meaningful scale.

III: A Wicked Problem and a Strategic Dilemma

Before examining why the Learning Health System has been so difficult to build, it is worth being precise about the nature of the problem itself. Not all hard problems are the same kind of hard. Some are difficult because resources are insufficient. Some are difficult because the right solution has not yet been found. The failure to operationalize the Learning Health System at scale is neither of these. It is something more structurally challenging, and naming it correctly matters because the type of problem determines what kind of thinking is adequate to address it.

In strategic management and organizational theory, a distinction is drawn between problems that are complicated and problems that are wicked. A complicated problem, however technically demanding, has a definable solution. Building an aircraft is complicated. The right answer exists, the variables can be enumerated, and expertise applied systematically will eventually produce the result. A wicked problem is different in kind, not just in degree. The concept was introduced by Rittel and Webber in their foundational 1973 paper “Dilemmas in a General Theory of Planning,”[5] which argued that problems of social policy cannot be solved using scientific-engineering approaches because they lack a clear problem definition and involve stakeholders with genuinely differing and legitimate perspectives. Wicked problems are not merely unsolved. They resist definitive formulation. Every attempt to solve them reveals new dimensions of the problem. Solutions cannot be tested in advance and cannot be undone cleanly once implemented. There is no single right answer, and the people working on the problem do not agree on what success would look like.

The challenge of building a Learning Health System is a wicked problem in precisely this sense. It is not a technology problem, though technology is implicated. It is not a regulatory problem, though regulation shapes the environment. It is not a funding problem, though funding matters. It is a problem that cuts across all of these domains simultaneously, involves stakeholders whose legitimate interests are in genuine tension with one another, and resists any solution that addresses only one of its dimensions. Researchers working in this space have noted that strategy scholars who attempt to address wicked problems using conventional approaches tend to build causal models that seek to optimize organizational success, an approach that ironically divorces the analysis from the very complexity that makes the problem wicked in the first place.[6]

Within this wicked problem, however, there is a more specific structure worth naming. The Learning Health System presents what might be called a strategic dilemma: a situation in which legitimate goods are in genuine tension with each other, and in which choosing to prioritize one value necessarily creates pressure on another. Patient safety and the imperatives of research require different things from a consent framework. The need for standardization conflicts with the need for clinical judgment. The value of data utility for population-level learning conflicts with individual privacy rights. The urgency of improvement conflicts with the rigor that improvement based on evidence requires. These are not tensions that can be dissolved by finding a smarter solution. They are structural features of the problem that any serious approach must hold in view simultaneously rather than resolving prematurely in favor of one side.

This distinction between a wicked problem and a strategic dilemma is not merely academic. It has direct implications for how we think about leadership and organizational design in this space. Wicked problems cannot be assigned to a committee and solved on a timeline. They require what the Cabreras would describe as thinking design rather than framework imposition: the cultivation of a quality of thinking in leaders and institutions that is capable of holding complexity, adapting continuously, and learning from the system rather than simply managing it. The Learning Health System is not waiting for the right policy. It is waiting for a different quality of organizational thinking. And that is a problem that systems thinking, properly understood, is specifically designed to address.

IV: Organizations as Complex Adaptive Systems — The Cabrera Lens

Understanding why the Learning Health System has been so difficult to operationalize requires more than a catalogue of obstacles. It requires a way of thinking about organizations that is adequate to their actual nature. Most health systems have been designed and managed as if they were complicated machines: hierarchical, controllable, and optimizable through the right combination of process improvement, technology, and incentive alignment. The persistent failure of that approach to produce genuine organizational learning suggests that the underlying model of what a health system is may itself be the problem.

Drs. Derek and Laura Cabrera at Cabrera Research Lab have spent decades developing and empirically grounding a different model. Their work, elaborated in Flock Not Clock and in an extensive body of peer-reviewed research,[3] begins from a foundational premise: all organizations, regardless of their formal structure, are complex adaptive systems. A complex adaptive system, or CAS, is composed of autonomous agents whose individual behaviors interact to produce collective, emergent outcomes that cannot be predicted or controlled by managing the agents individually.[13] The agents are not cogs in a machine executing instructions from above. They are people making decisions, moment by moment, in response to the conditions and incentives around them. The organization does not produce its outcomes by command. It produces them by emergence, as the aggregate result of countless individual decisions made at every level of the system every day.

This changes how we think about organizational design. If a health system is a complex adaptive system, then the question of how to build a learning culture inside it is not primarily a question of policy, technology, or incentive structure, though all of these matter at the capacity level. It is a question of what conditions and orientations the autonomous agents in the system are operating under, and whether those conditions make learning a natural emergent outcome of their daily work or an additional burden layered on top of everything else they are already asked to do.

The Cabreras developed a thinking design structure called VMCL, standing for Vision, Mission, Capacity, and Learning, to help leaders understand and shape the four functions that any organization must perform in order to move purposefully toward its goals.[4] VMCL is not a framework to be implemented as a checklist or adopted as a rebranding exercise. It is a thinking design lens, a way of seeing clearly what an organization is actually doing across its four essential functions, and whether those functions are genuinely aligned with each other and with the organization’s deepest purpose. The value is in the quality of thinking it cultivates in leaders, not in the mechanical application of its categories. Of the organizational design frameworks the author has encountered across three decades of operational leadership, the Cabrera VMCL structure is the most useful for making visible what is actually happening inside a complex organization and why.

Vision is a destination, not an action. It is a picture of a specific future state, clear enough to be genuinely directional and distant enough to be genuinely aspirational. Vision is not a description of what the organization does or how it operates. It is the answer to the question: if everything this organization is trying to accomplish were fully realized, what would the world look like? Most organizational vision statements fail this test entirely. They are the product of committee processes in which boards, executives, communications professionals, and legal reviewers each add words until the original impulse toward meaning has been buried under qualifications and compromises. The result is statements that are long, passive, and forgettable, that could belong to any organization and therefore belong to none, and that no frontline worker could honestly say lives in their hearts and minds while doing their job. Genuine vision is short enough to remember, true enough to feel, and clear enough to orient behavior without requiring a footnote.

Mission is the mechanism by which vision becomes real. In the VMCL structure, mission is not a values statement or a description of organizational purpose. Mission is the simple rules: the small number of repeatable, measurable actions that, when enacted consistently by autonomous agents throughout the organization, produce movement toward the vision as an emergent outcome.[12] The Cabreras draw on complex adaptive systems science to make a counterintuitive but empirically grounded argument: large-scale coordinated behavior in complex systems does not require elaborate instructions or top-down control. It requires simple rules, followed by many agents, repeatedly. Consider the wave at a stadium. No policy memo was issued. No training was conducted. The behavior that ripples across tens of thousands of people in a single coordinated arc emerges from a small number of simple rules enacted by each individual: watch your neighbor, rise when they rise, sit when they sit, raise your hands. The wave is not managed into existence. It emerges. Mission, properly conceived, functions the same way inside organizations. If the simple rules of mission are well designed, genuinely understood, and authentically shared, coordinated movement toward vision emerges from the collective behavior of autonomous agents without requiring command and control of every decision. The parallel failure mode matters equally: if mission consists of a lengthy statement written for external audiences rather than a small number of actionable rules that people can actually carry in their heads, then the organization’s agents have nothing simple to enact, and the coordinated movement that vision requires cannot emerge.

Capacity is the infrastructure, systems, tools, skills, and resources that enable the mission to be carried out. It is what the organization has built, or inherited, or been forced to adopt, to allow its agents to do the work that produces the vision. Capacity includes technology, physical infrastructure, trained personnel, financial resources, data systems, and organizational structures. The critical insight in the VMCL framework is that capacity must be aligned with mission. Capacity built for a different mission, however large, sophisticated, or expensive, does not support the mission it was not designed to serve. It actively competes with it, consuming the time, attention, and energy of the autonomous agents who are supposed to be carrying out the simple rules that produce the vision. The question of whether a health system has the capacity to be a Learning Health System is therefore not simply a question of whether it has electronic health records, data analytics capabilities, or quality improvement staff. It is a question of whether those investments were designed and are being used in service of a learning mission, or whether they were designed for other purposes entirely and are now being asked to serve a mission they were never built to support.

Learning is the function that makes the other three adaptive rather than static. In the VMCL framework, learning is the organization’s capacity to gather honest feedback from its own behavior and from its environment, assess that feedback against its vision and mission, and actually change what it is doing as a result.[4] In the specific context of the Learning Health System, this has a precise meaning that goes beyond general organizational learning or individual professional development. Learning in the LHS sense is the cycle of gathering clinical and operational data generated within the health system itself, subjecting it to rigorous analysis, producing knowledge about what is actually working for actual patients in this actual system, and feeding that knowledge back into changed clinical practice in ways that improve patient outcomes. The unit of learning is the system. The measure of learning is not the number of insights generated or reports published. It is whether practice changes and whether patients do better as a result. Quality dashboards that nobody acts on, annual reports that circulate among administrators without altering clinical behavior, and research findings that never make it from the journal to the bedside are all symptoms of an organization that has the appearance of learning without the substance of it.

These four functions are not sequential steps. They are simultaneous and mutually dependent. Vision without mission produces inspiring rhetoric that changes nothing. Mission without vision produces activity without direction. Capacity without aligned mission and vision produces expensive infrastructure that serves the wrong ends. And Learning without the other three produces insight that has no home in the organization’s structure and no pathway to changing behavior. The question the VMCL lens asks of any health system is not whether these four functions exist in some form, because they all do in every organization. The question is whether they are genuinely aligned with each other, whether they are all oriented toward the same destination, and whether that destination is honestly about learning and patient outcomes or about something else dressed in that language.

V: Three Conditions Hostile to Learning

The VMCL lens developed by the Cabreras does not merely describe what a well-functioning organization looks like. It also provides a diagnostic structure for understanding where and why organizational function breaks down. When a complex adaptive system is failing to move toward its vision, the failure can almost always be located in one or more of the four functions: the vision is unclear or not genuinely shared, the mission lacks simple rules that agents can actually carry and enact, the capacity is misaligned with the mission, or the learning function is absent, performative, or structurally disconnected from the decisions that govern practice.

Applied to the challenge of building Learning Health Systems in the United States, this diagnostic structure surfaces something important. The barriers most frequently discussed in clinical, policy, and public discourse cluster with particular intensity around the Capacity and Learning functions. Three conditions in particular have emerged with enough consistency across enough professional, policy, and clinical circles to warrant focused examination here. They are not presented as the only barriers. The published literature names others, including interoperability failures, governance gaps, funding misalignment, and cultural resistance to change.[15] They are presented because each is vivid, well-documented, and together they do something more important than illustrate three separate problems. They form a causal chain.

That chain runs as follows. Electronic health record systems were designed for billing, documentation, and regulatory compliance rather than for clinical care or learning. They impose structural friction on the daily work of every physician in the country. Payer interference through prior authorization requirements compounds that friction, consuming hours of clinical time every week, systematically overriding clinical judgment, and producing a persistent experience of professional constraint that no amount of individual resilience can fully absorb. Together these two systemic forces create the organizational conditions that produce physician burnout at scale. Burnout is not an independent variable sitting alongside EHR burden and payer interference. It is the human output of a system that has been designed at the capacity level for the wrong mission. And a system whose agents are burned out cannot learn, because learning requires the cognitive availability, the reflective capacity, and the institutional trust that survival mode structurally forecloses.

This is what the Cabreras mean when they say that the system is what the system does. If the system consistently produces burned-out physicians, demoralized care teams, and a clinical workforce increasingly oriented toward self-preservation rather than adaptive engagement, that is not a failure of individual character or professional commitment. It is the system performing as it was designed to perform, optimizing for throughput, administrative control, and reimbursement rather than for learning and patient outcomes. Understanding the three conditions in sequence, rather than as a parallel list, is essential to understanding why the organizational design problem is as deep as it is.

Electronic Health Records: Capacity Built for the Wrong Mission, Sitting on the Right Data

The widespread adoption of electronic health records in the United States was accelerated by the Health Information Technology for Economic and Clinical Health Act of 2009 [23]. As of 2021, 96 percent of nonfederal acute-care hospitals and 78 percent of office-based physicians used an EHR, making these systems integral to routine clinical practice.[10] On its face, this represents exactly the kind of data infrastructure that a Learning Health System requires. A system that captures clinical data at scale, across encounters, patients, and populations, is precisely what the knowledge generation and data functions of the LHS cycle depend on. In this narrow sense, American healthcare has already built something the Learning Health System needs. The data is there. Decades of patient encounters, clinical decisions, treatment courses, and outcomes are sitting in these systems at a scale that would have been unimaginable to the architects of the NAM’s 2007 vision.

The problem is not the existence of the data. The problem is everything surrounding it.

EHRs were not primarily designed for learning. They were designed for billing, documentation, and regulatory compliance. The gap between the data infrastructure a learning mission requires and the data infrastructure that exists is not a gap in hardware or software capability. It is a gap in design intent, and that gap has consequences that run in two directions simultaneously. The first is the burden the systems impose on the clinicians who must feed them. A recent scoping review published in the Journal of Evaluation in Clinical Practice found that clinicians now spend an estimated one-third to one-half of their working day interacting with EHR systems, translating to over $140 billion in lost care capacity annually.[10] The same review found that clinicians frequently experience significant workflow disruptions caused by poorly designed interfaces, leading to task-switching, excessive screen navigation, and fragmented critical information that necessitates workarounds and increases the risk of documentation errors. Research published in JAMA found that physicians spend approximately 36.2 minutes documenting in the EHR for every 30-minute office visit [24], meaning the administrative burden of capturing an encounter now routinely exceeds the clinical time of the encounter itself.

The second consequence is less frequently discussed but equally important for the Learning Health System argument. The data that EHRs generate is not clean learning data. It is documentation data, structured around billing codes, shaped by prior authorization requirements, and produced through documentation processes that clinicians have adapted, often through workarounds, to minimize burden rather than to maximize clinical accuracy. The result is a paradox at the heart of the LHS challenge: American healthcare is sitting on an extraordinary volume of clinical data that a learning system would need, and simultaneously that data is less useful for learning than its volume suggests, because the processes that generated it were optimized for reimbursement rather than for clinical fidelity.

Mining that data for genuine learning insights would require significant investment in data science, informatics, and clinical expertise working in close collaboration. It would require clinicians who have the time, the cognitive availability, and the institutional support to participate in that work. It would require organizations that have aligned their capacity with a learning mission rather than a billing mission. And it would require a workforce that has not been burned out by the very systems that are generating the data in the first place. The EHR is not an obstacle to the Learning Health System in spite of the data it holds. It is an obstacle in part because of the conditions it has created around that data. The data exists. The capacity to act on it does not, because the system has consumed that capacity in the process of generating the data.

In VMCL terms this is a Capacity problem of a specific and frustrating kind. The investment has been made. The infrastructure is in place. But it was built for the wrong mission, and the friction it generates spills directly into the clinical encounter itself, into the relationship between physician and patient, and into the professional experience of every clinician who ends the day staring at a screen long after the last patient has gone home.

Payer Interference: External Rules Overriding Internal Mission

If EHR burden creates structural friction in the tools physicians use, payer interference through prior authorization creates structural friction in the decisions physicians are permitted to make. Together they constitute a double compression of clinical capacity that is difficult to fully appreciate from outside the daily experience of practicing medicine in the United States today.

The American Medical Association conducts an annual nationwide survey of 1,000 practicing physicians on the burden of prior authorization. The 2024 findings are both consistent with prior years and striking in their severity.[9] Physicians reported completing an average of 39 prior authorization requests per physician per week, consuming an average of 13 hours of physician and staff time. Ninety-three percent of physicians reported that prior authorization delays access to necessary care. Eighty-nine percent reported that it contributes to burnout. Ninety-four percent said it has a negative impact on patient clinical outcomes. More than one in four reported that prior authorization caused a serious adverse event for a patient in their care. Seventy-eight percent reported that it often or sometimes results in patients abandoning a recommended course of treatment entirely. Forty percent of practices have hired staff whose exclusive function is managing prior authorization requests.

In the language of complex adaptive systems, prior authorization represents external agents, payers and insurers, injecting rules into the system that redirect the behavior of internal agents, physicians and care teams, away from what their clinical training, judgment, and the available evidence would support, and toward what the external agent will reimburse. The internal simple rules of the care delivery mission are being overridden at the point of care by administrative requirements that serve a different set of goals entirely. This is not a marginal disruption. At 39 prior authorization requests per physician per week, it is a structural feature of the environment in which clinical work now happens.

The implications for the Learning Health System extend beyond the administrative burden. The LHS cycle depends on clinical practice generating data that reflects actual clinical judgment applied to actual patient needs. When a substantial proportion of clinical decisions are being shaped not by evidence and judgment but by prior authorization requirements, the data that clinical practice generates no longer cleanly reflects what works. It reflects what gets approved. The knowledge that a learning system could generate from that data is therefore systematically biased before it is ever analyzed. The learning loop is not merely slowed by payer interference. In important respects it is compromised at the source.

And when a physician has spent 13 hours in a week on prior authorization paperwork, on top of the hours already consumed by EHR documentation, the cumulative weight of that friction does not remain a professional inconvenience. It becomes a clinical emergency of a different kind entirely. It becomes burnout.

Physician Burnout: The Human Output of a Broken System

Physician burnout is not the beginning of the problem. It is the end of a chain that starts with organizational design decisions made far from the bedside. It is what happens when the agents of a complex adaptive system are placed inside a capacity structure so misaligned with the mission of care that adaptive engagement becomes unsustainable. The EHR consumes time and cognitive energy. Prior authorization consumes professional agency and clinical judgment. Together they produce a working environment in which the question a physician must increasingly ask is not what does this patient need but what will I be permitted to do, and how long will the paperwork take.

The data on physician burnout in the United States is not ambiguous. According to the Dr. Lorna Breen Heroes’ Foundation, 76 percent of healthcare workers reported burnout in 2020, and during the COVID-19 pandemic 69 percent of physicians experienced depression, with 13 percent reporting thoughts of suicide.[7] Physicians in the United States are more likely to die by suicide than physicians in other nations. The Physicians Foundation’s 2022 Survey of America’s Physicians found that burnout rates remain at 62 percent, significantly higher than the pre-pandemic figure of 40 percent in 2018, with no meaningful improvement in the intervening years.[8] Nearly 400 physicians die by suicide annually in the United States, a figure the research literature connects directly to stigma, fear of licensing repercussions, and untreated depression in a profession that has historically treated the need for mental health support as a professional liability.[7]

The Dr. Lorna Breen Heroes’ Foundation, established by the family of an emergency physician who died by suicide in April 2020 after treating patients during the early COVID-19 surge, has been explicit about the systemic nature of the problem. Individual support alone, the foundation states, does not address the causes of burnout. The underlying processes and systems within healthcare operations must be confronted.[7] That is a systems thinking argument made in plain language by people who lived the consequences. It points directly at the Capacity layer of the VMCL structure and asks why the system was designed this way and whether the people responsible for that design have fully reckoned with what it produces.

For the Learning Health System, burnout represents the final compression of capacity. Learning requires clinicians who can observe, reflect, contribute to knowledge generation, and adapt their practice in response to what the evidence is telling them. It requires agents who are present, engaged, and operating with enough cognitive and professional reserve to participate in something beyond the immediate transaction of care. Burnout forecloses that participation systematically, across specialties, settings, and the full arc of a clinical career. A system that is burning out its physicians at the rate American healthcare currently does is not a system that can learn. It is a system that is consuming its own capacity to improve.

The three conditions examined in this section are not a complete explanation of why Learning Health Systems have been so difficult to build. But they are a coherent one. They describe a system that has built the wrong capacity, allowed that capacity to be further distorted by external rule-making, and in doing so created the organizational conditions that make the human beings at the center of care less and less able to participate in the continuous learning that better care requires. The system is, in the most precise sense, doing exactly what it was designed to do. The question this paper is asking is whether it could be designed to do something different.

VI: Thinking Design, Not Framework Prescription

If the argument of this paper has been constructed carefully, the reader has arrived here with a specific kind of discomfort. The problem is real, well-documented, and serious. The VMCL lens has provided a coherent way of seeing why the Learning Health System has not been built at scale. The three conditions examined in Section V have illustrated, in concrete and citable terms, how the capacity layer of American healthcare has been so comprehensively misaligned with a learning mission that the human beings at the center of care are being systematically consumed by the friction of a system that was designed for other ends. The natural next question is: so what do we do about it?

This section is going to resist the impulse to answer that question with a prescription. That resistance is not evasion. It is the most honest and useful response available, and the reasons for it are worth stating plainly.

The wicked problem literature is clear that conventional problem-solving approaches are structurally inadequate to problems of the kind this paper has been examining. The Learning Health System is not waiting for the right policy intervention or the right technology platform or the right reimbursement model, though all of these matter and deserve serious attention. It is waiting for a different quality of organizational thinking in the people and institutions responsible for designing, leading, and reforming American healthcare.

The Cabreras make a distinction that is useful here. They differentiate between organizations that impose frameworks and organizations that develop genuine thinking capacity, the internal ability to see clearly, reason carefully, and adapt continuously in response to what the system is actually doing.[3] A framework can be adopted without changing the underlying quality of thought. A new software platform can be installed without changing the organizational culture that will use it. A new policy can be passed without changing the incentive structures that will determine whether it is followed in spirit or circumvented in practice. What cannot be faked, and what the Learning Health System actually requires, is the organizational capacity to ask honest questions about what the system is producing, to follow the answers wherever they lead, and to change course based on what is discovered.

Before any of that can happen, the system must be mapped. Not fixed. Not optimized. Mapped. This is a critical distinction. The problems do not precede the mapping. They emerge from it. A system cannot be improved by agents who cannot see it clearly, and seeing it clearly requires a specific and disciplined quality of thinking. The Cabreras offer exactly that through a cognitive framework called DSRP, standing for Distinctions, Systems, Relationships, and Perspectives.[19][21] DSRP describes four universal patterns of thinking that, when applied deliberately, allow a leader or organization to see a system as it actually is rather than as habit, assumption, or organizational mythology would have it appear. To understand what the system does, you must first understand what the system is. DSRP is the toolkit for that work.

Before reaching for solutions, the Cabreras ask leaders at every level to sit with a set of honest diagnostic questions:

Does your organization have a vision that is genuinely and specifically about the future it is trying to create, stated clearly enough that every person in the system, from the bedside nurse to the chief executive, could carry it in their hearts and minds while doing their job on any given day? Or does it have a statement written for a board presentation, long, passive, and laden with qualifications, that could belong to any organization and therefore belongs to none?

Does your organization have a mission in the specific sense of simple rules, repeatable actions that autonomous agents at every level of the system can enact without a manual, that would make learning a natural outgrowth of daily clinical practice? Or does it have a strategic plan, full of initiatives and objectives and key results, that bears no relationship to what a nurse or a physician or a data analyst actually does on a Tuesday morning?

Has your organization built capacity that is aligned with a learning mission, or has it built capacity for billing, documentation, and regulatory compliance and then asked that infrastructure to support learning as a secondary function while simultaneously burning out the people who are supposed to use it?

And does your organization have genuine learning mechanisms, honest feedback that actually changes clinical practice, that actually improves patient outcomes, that actually closes the loop between what the system discovers and what the system does? Or does it have quality dashboards and compliance reports and annual reviews that circulate among administrators without ever altering what happens in an exam room?

These are diagnostic questions, not rhetorical ones. They are the questions that thinking design asks of any organization that claims the Learning Health System as an aspiration. They are uncomfortable because for most health systems, across most of these dimensions, the honest answer is not encouraging. And they are important precisely because the discomfort they produce, if it is held rather than resolved prematurely, is the beginning of genuine organizational learning.

The four DSRP patterns work as follows.

Distinctions are the act of identifying what something is and what it is not, drawing a boundary between a thing and everything that is not that thing. In the context of the Learning Health System, making clear distinctions means being honest about what a learning system actually is, and separating it clearly from what merely resembles it. A teaching hospital is not a learning health system. A quality dashboard is not a learning mechanism. An EHR is not a learning infrastructure simply because it generates data. Without the discipline of making clean distinctions, organizations substitute the appearance of learning for the substance of it and never notice the difference.

Systems, in the DSRP sense, is the recognition that any phenomenon of interest is simultaneously a part of larger wholes and a whole composed of smaller parts, and that understanding it requires attending to both levels at once.[20] In the healthcare context, physician burnout is a part of a larger system of capacity failures, and it is itself a whole composed of contributing conditions including EHR burden, prior authorization load, professional isolation, and the erosion of clinical agency. Understanding both the part and the whole simultaneously is what prevents the mistake of treating burnout as an individual problem rather than a systemic one.

Relationships are the causal and dynamic connections between elements of a system, the action and reaction that link one condition to another and produce the emergent outcomes the system generates.[20] The causal chain this paper has traced, from EHR misdesign through payer interference to burnout to the collapse of learning capacity, is a relationships argument. These three conditions are not parallel and independent. They are sequentially and causally connected, and intervening in one without attending to the others will produce incomplete and temporary relief at best.

Perspectives are the recognition that every observation of a system is made from a point of view, and that changing the perspective from which a system is examined reveals different features, different problems, and different possibilities.[20] The Learning Health System has been examined primarily from the perspectives of bioethicists, health policy scholars, and informatics researchers. Those are valuable perspectives. But they are not the perspective of the burned-out emergency physician at the end of a 13-hour shift, or the primary care doctor who spent two of those hours on prior authorization paperwork, or the patient whose recommended treatment was abandoned because the approval process took too long. Bringing multiple genuine perspectives into the analysis is not a concession to inclusivity. It is an epistemic requirement for seeing the system accurately.

Together these four patterns constitute the cognitive foundation for systems mapping, the act of making the system visible in a form that allows its parts, relationships, boundaries, and embedded perspectives to be examined honestly and collectively.[17] Making the system visible before reaching for a solution is not a preliminary step on the way to the real work. It is the real work.[17][18] This paper is, in one sense, a partial map of a system. It does not resolve the wicked problem of the Learning Health System. It attempts to make that problem more visible, more precisely named, and more honestly held, in the conviction that a system cannot be improved by agents who cannot see it clearly.

VII: Building the Ecosystem

This paper has traced a specific arc. It began with a conversation, with the recognition that a system described as healthcare has organized itself primarily around sick care, and that a system capable of learning from its own practice toward the goal of genuine health remains largely unbuilt. It named that gap as a wicked problem, structurally resistant to the kinds of solutions that work on complicated problems. It introduced a thinking design lens, VMCL, that reveals where and why the organizational design of American healthcare has been misaligned with a learning mission. It examined three conditions, EHR burden, payer interference, and physician burnout, not as a comprehensive catalogue of everything wrong but as a coherent illustration of a system doing exactly what it was designed to do, which is the wrong thing. And it argued that before solutions can be designed, the system must be mapped, using the cognitive tools of Distinctions, Systems, Relationships, and Perspectives, so that what is actually happening can be seen clearly by the people responsible for changing it.

What comes next is not a conclusion in the conventional sense, because wicked problems do not conclude. They develop. They yield to sustained, cross-disciplinary, honest engagement over time, or they do not yield at all. And that engagement, to be genuine, cannot be organized as a committee or delegated to a working group. It has to function as an ecosystem.

An ecosystem, in the organizational sense, is not simply a collection of stakeholders. It is a community of interdependent actors whose collective behavior produces outcomes that no single actor could generate alone, and whose health depends on the health of every part. The Learning Health System cannot be built by clinicians alone, or technologists alone, or policymakers alone, or systems thinkers alone, because each of those communities has a partial view of the system, and partial views applied with confidence have contributed to the problem as much as to any solution. What the Learning Health System requires is an ecosystem response, one in which diverse and genuinely interdependent actors develop a shared sense of responsibility for the knowledge the system is capable of generating and for the patients whose outcomes depend on whether that knowledge is actually used.

Several conditions define what a functional ecosystem for this work looks like.

Patients must be active contributors, not symbolic participants. The Stanford course materials that informed this paper make a point worth stating directly: in the Learning Health System, every patient is also a research participant, and their data represent an opportunity to learn.[11] The ethical framework developed by Ruth Faden, Nancy Kass, and their colleagues[25] argues that patients have not only rights but obligations within a learning health system, specifically an obligation to contribute to the knowledge that the system generates for their benefit and for the benefit of others, particularly when the risk to them is minimal. Designing health systems that honor that relationship, rather than treating patients as subjects to be protected from the learning process, is one of the most important organizational design challenges the field faces.

Health system leaders must be willing to ask honest questions about what their organizations are actually producing. The wicked problem of the Learning Health System will not be solved by a consultant engagement, a technology platform, or a strategic planning cycle. It will be addressed, partially and incrementally, by leaders who are willing to hold the discomfort of answers that do not reflect well on past choices and design differently in response to what they discover. That requires vision that is genuinely about learning and patient outcomes. It requires mission in the form of simple rules that every agent in the organization can carry and enact. It requires capacity built and aligned for the right purpose. And it requires learning mechanisms that are honest, structural, and actually connected to changed practice.

The ecosystem must also have a convening architecture. Calling for cross-disciplinary engagement on a wicked problem is easy. Designing the conditions under which that engagement can actually happen is considerably harder. In June 2020, the author designed and led SparkJam 2020, a statewide initiative convened through The Rocket Factory in partnership with Activation Capital, the VCU da Vinci Center for Innovation, and other Virginia-based organizations.[22] The initiative brought together entrepreneurs, technology visionaries, business strategists, and community leaders to collaborate in real time on solutions to challenges facing small businesses during the pandemic. The methodology that made it work rested on a specific structural logic: a small group of influential leaders set the agenda, identified the most consequential problems, and recruited a broader population of participants whose direct knowledge and diverse perspectives were needed to work those problems in depth. Structured sessions generated insights that no individual perspective could have produced alone. The broader group returned its work to the leadership tier for synthesis and prioritization, and working groups carried specific initiatives forward. That architecture, a credible leadership tier, broad and diverse participation, structured synthesis, and sustained working group commitment, is precisely what ecosystem convening for the Learning Health System requires.

This paper is itself a beginning and not an answer. It is a partial map of a system far larger and more complex than any single document can represent. What it hopes to contribute is a quality of framing adequate to the problem’s actual complexity. The ecosystem that the Learning Health System requires is waiting to be convened. The methodology exists. The will to build it is what remains to be found.

VIII: AI Implications — When Upstream Conditions Corrupt Downstream Intelligence

The organizational design argument this paper has been making has urgent implications that extend beyond health system walls and into the ambitions of every health technology company, AI developer, and investor currently betting that data-driven tools will transform American healthcare. The case for cross-disciplinary convening made in Section VII is not merely about improving care delivery. It is also about creating the organizational conditions under which technology can actually function as promised. Because the technology being deployed into American healthcare today is only as trustworthy as the data it learns from. And that data was produced by the system this paper has been describing.

Any health technology company seeking to leverage healthcare data to improve patient outcomes must first understand and reckon with what is happening upstream of that data. The organizational conditions under which data is generated determine what that data actually contains. This is not a theoretical concern. It is an engineering one, with direct consequences for patient safety.

Machine learning models learn from the data they are given. They do not evaluate the conditions under which that data was produced. They do not know whether the physician who entered a clinical note was on hour eleven of a shift, copying and pasting from a prior visit to manage an impossible documentation burden, or making a fully considered clinical judgment after a thorough examination. They do not know whether a treatment decision reflected the best available evidence or the path of least resistance through a prior authorization process. They do not know whether a diagnostic code was selected because it most accurately described the patient’s condition or because it was the code most likely to be reimbursed. The model sees the data. It cannot see the system that produced it. That is the job of the humans who build and deploy these tools. And it is a job that is not yet being done with sufficient rigor or honesty in the current wave of enthusiasm for AI in healthcare.

A well-known illustration in machine learning circles, included in the Stanford AI for Healthcare coursework that is part of this author’s ongoing study,[31] captures the failure mode precisely. During the Cold War, the US military hired computer scientists to develop a model that could identify Russian tanks in photographs. The model performed perfectly on the test set. In a live field test it failed completely, performing worse than random guessing. The reason: Russian tank photographs had been taken in winter conditions and American tank photographs in summer conditions. The model had not learned to identify tanks. It had learned to identify weather. It was, in the precise technical sense, a weather classifier dressed as a tank detector.[31]

The same failure mode has been documented in clinical settings. A machine learning model developed to detect pneumonia from chest X-rays outperformed human radiologists in controlled testing. In a small clinical deployment it failed. The model had learned to use the L marker, a physical positioning marker visible in the X-ray images, as a signal to distinguish between the two hospital systems in its training data. One hospital had a one percent prevalence of pneumonia. The other had a 34 percent prevalence. The model did not need to read the X-ray clinically. It learned to read the marker institutionally, and used that artifact rather than any clinical feature to predict pneumonia.[31] It was not learning medicine. It was learning to tell the hospitals apart.

These failures share a common structure. In each case the model learned the wrong signal because the training data encoded something other than the clinical reality the model was supposed to capture. The model was not broken. The data was. And the data was compromised not by random noise but by systematic, directional bias baked into the conditions under which it was produced. This is precisely what the three conditions examined in Section V create for any AI or machine learning system trained on American healthcare data at scale.

It is worth noting that the organizational conditions examined in this paper represent one category of the data bias problem in healthcare AI, and not the only one. The research literature identifies additional sources of bias that compound what has been described here, including the dynamic nature of medical practice over time, which causes historical EHR data to accumulate outdated correlations and effectively expire as a reliable training source as clinical practices evolve, and the demographic non-representativeness of many health system datasets, in which race, ethnicity, gender, and socioeconomic status are inconsistently captured or reported across studies, raising serious questions about whether AI models trained on such data can perform equitably across the full diversity of patients they will ultimately serve.[31]

When 90 percent of clinicians report using copy-paste functionality to manage documentation burden, and when by one estimate 50 percent of the text in a given clinical note is duplicated from prior notes,[27][28][29] the clinical notes that constitute training data for natural language processing models are not accurate records of clinical reasoning. They are records of documentation behavior under pressure. When prior authorization requirements shape which treatments are administered and which are abandoned, the treatment decisions that feed outcome models do not reflect clinical judgment applied to patient need. They reflect what the payer approved. When burned-out physicians experiencing cognitive fatigue make more documentation errors, a connection the research literature supports directly,[30] the signal in the data degrades in direct proportion to the degradation of the workforce producing it.

The research on EHR data quality confirms that these are not marginal concerns. A systematized review published in 2025 examining EHR data quality in critical care settings found that missing data rates exceeded 80 percent for some variables, that EHR-related medication errors comprised 34 percent of all medication errors in ICUs with one-third having life-threatening potential, and that copy-paste prevalence reached 82 percent in residents’ progress notes.[26] The same review found direct and measurable consequences for machine learning: sepsis detection models that achieved strong performance in internal validation dropped significantly in external validation under real-world conditions, a degradation the authors attributed directly to data quality issues pervasive in the underlying EHR data.[26]

The Stanford coursework poses the right question directly: the issue is not whether the data exists. Medical data now doubles every eight to twelve months and there is more of it than ever before. The better question is whether that data is actually usable for the intended purpose.[31] In the current organizational state of American healthcare, the honest answer is not exactly.

This does not mean AI has no role in healthcare. It means the role AI can play is constrained and shaped by the organizational conditions that produced the data it learns from. A 2025 perspective published in npj Health Systems argues precisely this point, noting that while the LHS ecosystem has been well described and its potential widely endorsed, operationalizing the LHS in the era of artificial intelligence requires deliberate attention to data governance, workforce development, and institutional design, the same organizational prerequisites this paper has been examining.[14] The organizational design work this paper has been describing, building genuine Learning Health Systems with aligned vision, mission, capacity, and learning functions, is not merely a clinical improvement agenda. It is the prerequisite for trustworthy AI deployment in healthcare. A health system that has not addressed the upstream conditions producing biased data cannot deploy AI safely or effectively. It will automate the distortions already present in its data and present the result as intelligence. Health technology companies that build on that foundation without looking upstream are not just taking a technical risk. They are taking a patient safety risk. And they are building businesses on data they do not fully understand.

IX: Strategic Implications — The Cost of Not Learning

This paper has operated at two levels simultaneously, and it is worth naming that distinction clearly before drawing it to a close. At the macro level, the Learning Health System is a vision for what American healthcare as a sector could become: a system in which knowledge generation is so embedded in the delivery of care that improvement becomes continuous, self-reinforcing, and oriented genuinely toward the people the system exists to serve. At the micro level, it is an organizational design challenge that must be addressed institution by institution, health system by health system, through specific and deliberate choices about vision, mission, capacity, and learning. The wicked problem lives at the macro level. The work of addressing it happens at the micro level. And the cost of not doing that work accumulates at both levels simultaneously, in individual clinical encounters that produce biased data, in technology deployments built on compromised foundations, in physicians who leave the profession, and in patients who do not receive the care the system was capable of providing if it had been designed to learn.

Gil Bashe argued that American healthcare is not failing for lack of innovation, investment, or talent. It is failing because it has lost sight of the people it exists to serve.[2] This paper has tried to show that losing sight of people and losing the organizational capacity to learn are not two separate failures. They are the same failure, expressed differently depending on where you are standing in the system. The burned-out physician who copies and pastes a clinical note at the end of an impossible shift has not lost sight of their patients. The system that created those conditions has. The EHR that generates data optimized for billing rather than clinical fidelity has not lost sight of patients. The design decisions that produced it have. The AI model that learns the wrong signal from compromised training data has not failed its patients. The upstream conditions that corrupted the data before it ever reached the model have.

The cost of not learning is not abstract. It is clinical. It is financial. It is technological. And it is human. At the macro level it is a sector that has spent nearly two decades describing a vision of continuous learning and improvement while building the organizational conditions that make that vision structurally unreachable. At the micro level it is every health system that has adopted the label of a Learning Health System without asking honestly whether its vision is felt, its mission is enacted, its capacity is aligned, and its learning loops actually close. The gap between those two things, between what is said and what is designed, is where patients fall through.

This paper has not proposed a solution. It has drawn a map. The map shows a system doing exactly what it was designed to do, which is the wrong thing, and it names the organizational thinking, the VMCL lens, the DSRP cognitive tools, the systems mapping discipline, that would allow leaders at every level to see that clearly and begin designing differently. It has also named what is at stake for those who choose not to look. For health system leaders the cost of not learning is an organization that optimizes toward the wrong destination and calls it excellence. For policymakers the cost is interventions that address symptoms without touching causes. For health technology companies the cost is products built on data they do not understand, deployed into systems they have not mapped, producing outcomes they cannot fully explain or defend. And for patients the cost is a system that was capable of learning how to serve them better and chose, through a thousand organizational design decisions made without that possibility in mind, not to.

The Learning Health System is not an idea whose time has not yet come. It is an idea whose organizational prerequisites have not yet been built. Building them is the work. It is hard, sustained, cross-disciplinary, and uncomfortable. It requires the kind of thinking this paper has been describing: honest, structural, willing to see the system as it is rather than as its mission statements describe it. It requires leaders at the macro level of American healthcare policy and at the micro level of every individual health system who are willing to ask whether they are designing for learning or designing for something else and calling it learning.

The conversation is open. The map is incomplete. The cost of not continuing it is borne by patients. That is reason enough to begin.

Citations

[1] Olsen, L.A., Aisner, D., and McGinnis, J.M., editors. Institute of Medicine (US) Roundtable on Evidence-Based Medicine. The Learning Healthcare System: Workshop Summary. Washington, DC: National Academies Press, 2007. PMID: 21452449. DOI: 10.17226/11903. Available at: https://pubmed.ncbi.nlm.nih.gov/21452449/ and https://www.ncbi.nlm.nih.gov/books/NBK53494/

[2] Bashe, Gil. Healing the Sick Care System: Why People Matter. Thought Leader Press, February 1, 2026. https://www.amazon.com/Healing-Sick-Care-System-People/dp/1613431805

[3] Cabrera, Derek and Laura Cabrera. Flock Not Clock: Design, Align, and Lead to Achieve Your Vision. Plectica LLC, 2018. ISBN: 978-1948486019. https://www.amazon.com/FLOCK-NOT-CLOCK-DESIGN-ACHIEVE-ebook/dp/B07DFPWTDS

[4] Cabrera Research Lab. VMCL Overview. Cabrera Research Lab Blog. https://www.cabreralab.science/blog/categories/vmcl

[5] Rittel, Horst W.J. and Melvin M. Webber. “Dilemmas in a General Theory of Planning.” Policy Sciences, vol. 4, 1973, pp. 155-169.

[6] Grewatsch, Sylvia, Steve Kennedy, and Pratima Bansal. “Tackling Wicked Problems in Strategic Management with Systems Thinking.” Strategic Organization, 2023. https://journals.sagepub.com/doi/10.1177/14761270211038635

[7] Dr. Lorna Breen Heroes’ Foundation. “Burnout.” https://drlornabreen.org/burnout/

[8] The Physicians Foundation. “2022 Survey of America’s Physicians.” https://physiciansfoundation.org/press-releases/npsa-day-2022/

[9] American Medical Association. “2024 AMA Prior Authorization Physician Survey.” https://www.ama-assn.org/system/files/prior-authorization-survey.pdf

[10] “Usability Challenges in Electronic Health Records: Impact on Documentation Burden and Clinical Workflow: A Scoping Review.” Journal of Evaluation in Clinical Practice, 2025. https://onlinelibrary.wiley.com/doi/full/10.1111/jep.70189

[11] Stanford University School of Medicine. Course materials on Learning Health Systems and research ethics. Materials on file with author.

[12] Cabrera Research Lab. “Simple Rules.” Cabrera Research Lab Glossary. https://help.cabreraresearch.org/simple-rules

[13] Cabrera Research Lab. “Complex Adaptive System (CAS).” Cabrera Research Lab Glossary. https://help.cabreraresearch.org/complex-adaptive-system-cas

[14] Steel, Peter A.D., Gabriel Wardi, Robert A. Harrington, and Christopher A. Longhurst et al. “Learning health system strategies in the AI era.” npj Health Systems, vol. 2, article 21, June 17, 2025. https://www.nature.com/articles/s44401-025-00029-0

[15] Tenenbaum, J.D. et al. “Accelerating a learning public health system: Opportunities, obstacles, and a call to action.” Learning Health Systems, 2024. https://onlinelibrary.wiley.com/doi/10.1002/lrh2.10449

[16] “Implementing the learning health system paradigm within academic health centers.” Learning Health Systems, 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10797573/

[17] Cabrera, D., Cabrera, L. “Why You Should Map: The Science Behind Visual Mapping.” White paper. Cabrera Research Lab, New York, 2018. https://www.researchgate.net/publication/349868707_Why_You_Should_Map_the_science_behind_visual_mapping

[18] Cabrera, L. and Cabrera, D. “Adaptive Leadership for Agile Organizations.” In Cabrera, D., Cabrera, L. and Midgley, G. (Eds.), Routledge Handbook of Systems Thinking. Routledge, London, UK, 2021. Draft preprint on file with author.

[19] Cabrera, Derek. “Distinctions, Systems, Relationships, and Perspectives (DSRP): A Theory of Thinking and of Things.” Evaluation and Program Planning, vol. 31, no. 3, 2008, pp. 311-317. https://pubmed.ncbi.nlm.nih.gov/18554716/

[20] Cabrera, Derek and Laura Cabrera. “DSRP Theory: A Primer.” Systems, vol. 10, no. 2, 2022. https://www.mdpi.com/2079-8954/10/2/26

[21] Cabrera Research Lab. “The Four Simple Rules of Systems Thinking: The Distinction Rule.” Cabrera Research Lab Blog, cabreralab.science. Available at: https://www.cabreralab.science/post/the-four-simple-rules-of-systems-thinking-the-distinction-rule

[22] The Rocket Factory. “The Rocket Factory Presents SparkJam 2020 to Benefit the Virginia 30 Day Fund.” PR.com, June 2020. https://www.pr.com/press-release/814285

[23] U.S. Department of Health and Human Services. “HITECH Act Enforcement Interim Final Rule.” Health Information Technology for Economic and Clinical Health Act, enacted as part of the American Recovery and Reinvestment Act of 2009, Public Law 111-5. Available at: https://www.hhs.gov/hipaa/for-professionals/special-topics/hitech-act-enforcement-interim-final-rule/index.html

[24] Rotenstein, L.S. et al. “System-Level Factors and Time Spent on Electronic Health Records by Primary Care Physicians.” JAMA Network Open, 2023. PMC: https://pmc.ncbi.nlm.nih.gov/articles/PMC10665969/

[25] Faden, Ruth R., Nancy E. Kass, Steven N. Goodman, Peter Pronovost, Sean Tunis, and Tom L. Beauchamp. “An Ethics Framework for a Learning Health Care System: A Departure from Traditional Research Ethics and Clinical Ethics.” Hastings Center Report, Special Issue, January-February 2013, pp. S16-S27. DOI: 10.1002/hast.134. PubMed PMID: 23315888. Available at: https://pubmed.ncbi.nlm.nih.gov/23315888/

[26] “Discovery of data quality issues in electronic health records: profound consequences for critical care medicine applications — a systematized review.” PMC, 2025. https://pmc.ncbi.nlm.nih.gov/articles/PMC12784561/

[27] Tsou, A.Y. et al. “Safe Practices for Copy and Paste in the EHR: Systematic Review, Recommendations, and Novel Model for Health IT Collaboration.” Applied Clinical Informatics, 2017. https://pubmed.ncbi.nlm.nih.gov/28830856/

[28] Urology Times. “Why is copying and pasting in the EHR such a problem?” February 2026. https://www.urologytimes.com/view/why-is-copying-and-pasting-in-the-ehr-such-a-problem-

[29] AMA Journal of Ethics. “How to Teach Good EHR Documentation and Deflate Bloated Chart Notes.” November 2025. https://journalofethics.ama-assn.org/article/how-teach-good-ehr-documentation-and-deflate-bloated-chart-notes/2025-11

[30] “Burnout Related to Electronic Health Record Use in Primary Care.” PMC, 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10134123/ [31] Stanford University School of Medicine. Course materials: Fundamentals of Machine Learning for Healthcare. Lecture transcripts on data bias, the Russian tank problem, clinical machine learning applications, medical data shelf life, and demographic representativeness in EHR-based AI research. Part of the AI for

The post Garbage In, Garbage Out: The Organizational Crisis Beneath Healthcare’s AI Gold Rush appeared first on Medika Life.

That is what makes the Digital Health & AI Innovation Summit (DHAI), taking place June 8-9 in Boston, distinctive within an increasingly competitive field of AI and innovation conferences. The Summit certainly offers a high-caliber program and noted speakers. However, its real value proposition beyond the agenda lies in the conversations and takeaways.

The carefully curated forum, organized by Amir Lahav, PhD, and World BI, is intentionally designed for a smaller community of roughly 500 attendees and more than 150 speakers and innovators. The result is that the connections become as valuable as the presentations.

That distinction matters more than many realize.

Artificial intelligence and digital health are moving at extraordinary speed. Health systems, pharmaceutical companies, regulators, investors and technology innovators are all trying to answer the same questions: How do we apply innovation responsibly while improving outcomes for patients and clinicians? How do we integrate AI into the R&D process? How can we leverage information technologies to accelerate the recruitment of the right people for clinical trials? The challenge is no longer simply technological capability. The challenge is implementation, governance and integration into the realities of care delivery.

Those questions are difficult to answer from the back row of a ballroom.

They are more likely to be explored over coffee between sessions, during a shared meal, or in quieter moments when people can challenge assumptions, exchange experiences and discuss what is actually working in health systems, research environments, and patient care settings.

That is where DHAI distinguishes itself.

The Power of Curated Expertise

What gives a conference enduring value is not only the quality of its speakers, but whether those speakers remain accessible enough to challenge assumptions, answer difficult questions and engage in unscripted dialogue. That is increasingly uncommon in modern health conferences, where influence often feels managed from a distance.

At DHAI, the proximity to the experience of 150 presenters is intentional.

The next era of health won’t be built in silos and it certainly won’t be forged by focusing on the hype. It requires leaders willing to share their failures alongside their successes, and their fears alongside their visions,” shares Amir Lahav, PhD, curator and DHAI organizer. “The DHAI Summit provides an exclusive, trusted space for these unfiltered conversations that rarely happen on public stages. This is an exclusive invitation to join the health AI  pioneers who are moving the needle and step into the room where the real trajectory of medicine is being shaped,” he adds.

For attendees seeking to understand how artificial intelligence is moving from experimentation to clinical reality, few conversations may prove more valuable than those surrounding the work of Dr. Dennis Wall at Stanford University. His groundbreaking efforts to apply AI to accelerate diagnostics, particularly in neurological and developmental conditions, reflect the growing intersection of machine learning and patient-centered medicine. In most settings, hearing someone like Wall speak might last 20 minutes. Here, the opportunity to continue the discussion between sessions may be equally important as the presentation itself.

The same can be said for leaders shaping the future of pharmaceutical innovation through AI. Thomas Fuchs, Chief AI Officer at Eli Lilly and Company, operates at the center of one of the most significant transformations underway in life sciences. His work integrating AI, pathology and drug discovery reflects how computational science is redefining therapeutic development. With pharmaceutical companies investing billions into AI-enabled research ecosystems, the ability to exchange perspectives directly with someone navigating those realities daily carries extraordinary value.

Precision medicine also takes on a more practical dimension through leaders such as John Axerio-Cilies, Chief Data and Technology Officer at Tempus AI. Tempus has become emblematic of how data science, oncology and artificial intelligence are beginning to reshape personalized medicine and diagnostics. Yet the real insight often comes not from keynote slides but from candid reflections on implementation challenges, physician adoption, workflow integration, and trust in AI-driven systems.

What also distinguishes the program is its recognition that health innovation no longer lives within traditional boundaries. Biology, computational science, organizational leadership and entrepreneurship are rapidly converging, creating entirely new expectations for how innovation enters the health ecosystem.

That reality becomes especially clear when considering trusted voices such as Tom Lawry, author of Hacking Healthcare and one of the most respected global advisors on AI strategy in health. For years, Lawry has argued that artificial intelligence alone cannot transform the delivery of care. Institutions themselves must evolve alongside technology. Leadership structures, workflow, culture and decision-making all become part of the innovation equation. His perspective reinforces an increasingly important truth: AI implementation is not fundamentally a technology challenge. It is a human challenge.

That same intersection between innovation and execution is reflected in the participation of Sally Ann Frank, Global Lead for Health & Life Sciences at Microsoft for Startups. Her work focuses on helping emerging companies move beyond promising ideas toward scalable and commercially viable solutions. Through strategy development, technical enablement and go-to-market support, she works directly with startups navigating the increasingly complex realities of AI, digital health and life sciences innovation. At a time when thousands of companies are entering the AI marketplace, Frank brings an unusually practical understanding of what separates experimentation from sustainable impact across the global health ecosystem.

The scientific and technical dimensions of the Summit are equally compelling. Arturo Loaiza-Bonilla, MD, MSEd, Co-Founder and Chief Medical AI Officer of Massive Bio, Network Chief of Hematology and Oncology at St. Luke’s University Health Network, whom I met recently during HITLAB Health Innovation Week in New York, champions an important evolution in medicine, where clinical leadership, oncology, data science and AI innovation are interconnected. His work sits at the intersection of precision medicine, clinical trials and responsible AI application, demonstrating how technology can expand access and support informed care decisions while keeping physicians and patients at the center of the experience.

The program also grounds innovation in the realities of patient care and health system operations. Through her leadership at NYC Health + Hospitals, Dr. Komal Bajaj has focused extensively on quality, equity and implementation within one of the nation’s largest public health systems. Her perspective introduces an important layer of realism into discussions that can sometimes become overly theoretical. AI may promise efficiency, but health systems must still ensure that innovation improves care delivery rather than complicates it.

That balance between aspiration and practicality is also reflected in leaders such as Lauren Li of Novartis, whose work in AI and innovation strategy demonstrates how global life sciences companies are integrating AI responsibly across research, development, and commercialization. The questions facing companies like Novartis are no longer whether AI will shape health innovation, but how to apply it responsibly while preserving scientific rigor and public trust.

Equally important to the DHAI agenda is the presence of Jeremy Walsh, Chief AI Officer at the Food and Drug Administration. At a moment when AI is moving rapidly into research, clinical decision support, diagnostics and operational health systems, regulatory leadership must provide oversight. FDA voice addresses a growing concern that innovation and governance cannot operate on separate tracks. The future of AI in health will depend not only on technological capability, but on transparency, accountability and safety. His perspective brings a policy and regulatory dimension to a conversation too often dominated by technology.

Taken together, these leaders represent more than expertise. They reflect the convergence of medicine, data science, biotechnology, health systems, patient engagement and policy. The global health ecosystem is entering a period in which barriers between disciplines are dissolving. Clinicians must understand data science. Technologists must better appreciate patient experience and the realities of workflow. Pharmaceutical leaders must think beyond molecules toward digital ecosystems and longitudinal patient engagement.

Why Human Connection Still Matters in the AI Era

That convergence changes the value of gatherings like this one. Large conferences often showcase these worlds side by side. Smaller curated forums create the possibility for those worlds to interact.

That dynamic is particularly important in digital health, where enthusiasm can sometimes outpace evidence. AI is neither a miracle nor a menace. It is a tool shaped by human intention, data quality and leadership. The most important conversations in AI and health today are not only about capability. They are about judgment.

How do we reduce physician burnout without depersonalizing medicine? How do we use predictive analytics responsibly? How do we ensure that innovation improves access rather than deepens disparities? How do we maintain trust while integrating increasingly autonomous technologies into patient care?

Those are conversations that require candor and mutual learning.

As someone attending and stepping to the stage during DHAI, I believe that may ultimately become its greatest differentiator. In health, relationships still matter. Communication still matters. Shared perspective still matters. Technology may accelerate insight, but human interaction remains essential to wisdom.

Health innovation does not advance through presentations alone. It advances through collaboration, challenge and conversation. Those exchanges between sessions often become the catalyst for strategies and unexpected ideas that continue long after this event comes to a close.

In a global health environment often defined by complexity, there is growing value in spaces where innovation feels ambitious and human. The DHAI appears designed to deliver that ROI.

The post The Value of Health AI Conferences Is No Longer the Stage. It’s the Hallway Conversation appeared first on Medika Life.

An operational model developed in Algeria may show a way that countries can make childhood vaccines more effective, more acceptable and more economical. It is a model that may provide a framework for middle-income countries across the globe, including many across the rest of Africa.

Six-in-one (or hexavalent) vaccines are cutting the number of clinic visits needed to prevent multiple life‑threatening infections and easing pressure on already stretched health systems. Growing economic evidence from Algeria and several Latin American countries suggests that while these vaccines may cost more upfront, the investment may be largely or entirely recovered through fewer appointments, streamlined logistics, and a reduction in cases of vaccine-preventable diseases and potential adverse events from vaccination. Yet the children who could benefit most – those living in low‑ and middle‑income countries are still the least likely to receive them, widening an avoidable gap between what modern vaccines can do and the protection children actually receive.

Most hexavalent vaccines save money in another way: they reduce the number of adverse events – side effects – that require treatment in a hospital or clinic. Acellular hexavalent vaccines include a type of protection against pertussis, or whooping cough, which is the gold standard for immunization in higher-income countries but has not yet been widely adopted beyond them.

With more than a decade of historical data supporting safety and efficacy, these acellular pertussis vaccines have a notable track record of improving vaccination coverage rates (VCR) and parents’ willingness to have their children protected, as they cause fewer painful adverse events [1].

Acellular pertussis (aP) vaccines are formulated using isolated antigens, which are purified and detoxified, thereby removing most of the components of the bacterium that cause undesirable reactions [2].

Most low- to middle-income countries still use whole-cell pertussis vaccines, which include a suspension of the entire inactivated Bordetella pertussis organism – some 3,000 antigens. Although the inclusion of far more antigens can result in a marginally higher immune response, the complexity of the vaccine leads to varying amounts of reaction-causing components between batches of vaccine and varying levels of protection [2].

The combination of more adverse events and variable efficacy means that developing countries bear a disproportionate share of the burden incurred through side effects. The side effects in children lead to an increased reluctance among parents to agree to future vaccines for their children and higher costs for the healthcare system. These problems often arise in healthcare systems that are inadequately equipped to deal with them.

Expert opinion from Algeria indicates that acellular hexavalent vaccination has improved vaccination coverage levels and simplified the vaccination schedule by reducing the number of appointments. It reduces the required number of immunization visits from ten to six. This eases pressure on overstretched health services, simplifies logistics and cold-chain management, and reduces indirect societal costs, including the time parents spend away from work.

Algeria is the third WHO African region country to adopt the acellular hexavalent vaccine into its national immunization schedule. Economic data from those countries and several in Latin America demonstrate that a rollout of the vaccine across African countries is not only possible but also economically advantageous [3,4,5,6].

Algeria’s vaccination metrics: an operational model for success

Vaccines have transformed child health in Algeria, as they have across the world. Since the initial introduction of vaccination in Algeria, followed by sustained efforts to expand the vaccination schedule, infant mortality rates have dropped dramatically from 163 per 1,000 live births in 1966 to 20 per 1,000 in 2023, a reduction of around 87% [7].

The percentage of children protected in Algeria has exceeded the targets set by the World Health Organization (WHO) for decades, with diphtheria, tetanus toxoid and pertussis (DTP) coverage consistently above 90% [8]. As in many countries, the COVID-19 pandemic disrupted healthcare systems, leading to a decline in vaccination rates, with DTP-3 coverage, a key measure of childhood vaccination, reduced to 77% in 2022 [9]. This situation was quickly improved, with coverage increasing to 92% by 2024 [9].

In 2022, three cases of polio caused by circulating vaccine-derived poliovirus type 2 were reported [10]. Rarely, the circulating vaccine-derived virus causes polio, highlighting the necessity of timely vaccination with IPV, with which these vaccine-derived cases do not occur [11].

The shift to hexavalent vaccination

Algeria’s shift from its former schedule to hexavalent vaccination was not a straightforward process. Initially, the five-in-one (or pentavalent) vaccine was replaced by a combination of the tetravalent vaccine (DTP-Hib) and the monovalent Hepatitis B vaccine (HBV), administered across 10 separate healthcare visits, necessitating additional appointments [12].

The change in the vaccination schedule resulted in delays in dose administration and a decrease in vaccination coverage. This issue was resolved with the introduction of the new schedule, which integrated an acellular hexavalent vaccine in 2023, reducing the number of required healthcare visits to six [13].

While polio vaccination was present in the previous schedule (with one IPV dose at 3 months and 3 OPV doses at 2, 4, and 12 months), inclusion as part of a hexavalent vaccine simplified the schedule (giving three doses of IPV at 2, 4, and 12 months associated with three OPV doses), helping to maintain the global strategy for polio eradication. The WHO recommends that all countries using OPV adopt a vaccination schedule with at least two doses of inactivated vaccine, which gives individual protection without the risk of vaccine-related polio [14].

The financial metrics of the switch

A recent whitepaper conducted a pharmacoeconomic analysis of the budgetary impact of transitioning from a whole-cell tetravalent vaccination schedule to an acellular hexavalent schedule. While the switch was associated with an increase in annual program expenditure of approximately 26 million Euros (around a 58% rise in upfront costs), this was substantially offset by nearly 19 million Euros in annual savings generated through the management of adverse events, improved logistics and transportation, and increased parental productivity [13]. Overall, roughly 73% of the upfront cost was offset by these savings.

Algeria is the latest in a series of examples where this is the case. The nominal, upfront cost of acellular hexavalent vaccines is typically higher than that of whole-cell vaccines; this has, in many cases, deterred countries from adopting them. However, there are many benefits at both the economic and systemic levels that recoup much of the costs of acellular hexavalent vaccines. In many instances, these costs are hidden and not factored into initial value calculations.

Similar experiences have been seen in other countries. In Argentina, Peru, and South Africa, the switch to hexavalent vaccines led to higher initial costs, but these were substantially offset by savings from fewer adverse events, lower programmatic expenses, and improved logistics. For example, in data from Argentina, roughly 90% of the initial investment into acellular hexavalent vaccines was recovered through fewer adverse event-associated costs and lower programmatic costs [15]. Peru reported a reduction in logistical costs by nearly 60%, with roughly 44% of the initial increase in costs recovered [16]. South Africa achieved overall savings of about 10 USD per child [3].

These calculations overlook benefits that are more difficult to quantify. For example, what costs are generated because of vaccines missed and infections caused by increased vaccine hesitancy on the part of parents. Across these settings, the higher upfront investment in hexavalent vaccines has proven to be economically viable, with much of the cost recouped through broader system efficiencies.

Programmatic benefits of hexavalent vaccination

Hexavalent vaccination offers the potential for simpler systems and higher levels of acceptance among patients. For the child, integrating six antigens into a single injection drastically reduces the number of needle sticks, alleviating injection-related anxiety and the prevalence of local reactions. This increases parental acceptance and helps to improve vaccination coverage.

Parents are relieved of the burden of coordinating multiple medical appointments, covering travel costs, and dealing with lost workdays. By reducing parental anxiety and the strain of repeated visits, combined vaccines help mitigate vaccine hesitancy within communities. This has been demonstrated in multiple studies across Africa, with investigations in Gambia and South Africa documenting concerns among parents about a child receiving more than two injections in a single visit [17,18]. Limiting the number of healthcare visits is also a crucial factor in increasing vaccine coverage in areas with limited healthcare infrastructure, such as those in rural southern Algeria.

For healthcare professionals, particularly in resource-limited settings such as rural areas in Africa, the adoption of combined vaccines helps to ease the administrative burden of multiple appointments. These formulations optimize consultation efficiency by drastically reducing the required administration time and simplifying inventory management [19].

The use of ready-to-use liquid vaccines, such as the hexavalents, has been shown to simplify and enhance the safety of the vaccination procedure when compared to vaccines that come as a powder that has to be reconstituted [20]. The preference for this approach among frontline workers is overwhelming: one study indicated that 97.6% of healthcare providers favored these liquid, combined formulations in their daily work [21]. Evidence supports this preference, demonstrating that the switch led to a dramatic reduction in administration errors (from 42.8% to 4%) and needlestick injuries (from 42.3% to 9.5%), while also yielding an average time savings of 1.1 minutes per dose [22].

The case for Hexavalent vaccination across Africa

Across the WHO Africa region, VCR has improved significantly over the last few decades; however, unfortunately, this improvement has stalled. The level of coverage for the third dose of DTP-containing vaccines, a standard benchmark for immunization system performance, has sat in the mid-70s for over a decade, with a current coverage of 76% [23].

This stagnation of the VCR is reflective of the ongoing issue of inequality. The gap in vaccine access runs not only between Africa and higher‑income regions, but also within the continent itself, where some countries consistently outperform others. Diseases, however, do not recognize borders; any outbreak that affects one country is likely to increase the risk to surrounding countries. Air travel enables a disease case to be spread to virtually any country in the world within just two days [24].

The COVID-19 pandemic was a clear example of the rapid spread in today’s world. Within a period of weeks, the virus spread from its origin in China to the entire globe, despite public health measures and lockdowns. With this in mind, any country that is falling behind on vaccination coverage becomes a weak link in a global chain where diseases can flourish and form reservoirs of cases that can allow diseases such as polio to spread unchecked.

Bringing vaccine equity to lower- and middle-income countries is therefore vital to addressing global health concerns. Hexavalent vaccination has demonstrated its ability to increase vaccine coverage in these countries. Among the WHO Africa region, Mauritius, which adopted hexavalent vaccination in 2017 [4] currently stands notably above the average for the region, with 96% coverage for the first dose of inactivated polio vaccine, and 93% for the benchmark based on DTP-containing vaccines [25].

Vaccine coverage translates into increased prosperity. Vaccination cannot be viewed as an inconvenient expense but as an investment. The WHO estimates that for every dollar spent, vaccination can yield a return on investment of around 54 USD – provided, of course, that the vaccines actually find their way into the arms of children [26].

As the Algerian case study demonstrates, higher upfront costs for acellular hexavalent      vaccines are often paid back by improvements in logistical efficiency, reduced healthcare burdens, and improved parental compliance. By bridging the gap between high- and low-income immunization standards through this investment, the life-saving benefits of gold standard vaccinations can become more than a privilege of geography, but a universal foundation for human health.

[This consensus paper is based on the findings of a white paper discussing the findings of a group of vaccination experts focusing on paediatric immunisation, supported by Sanofi. Intended for professional use.]

Reference list

1. Boisnard, F., Manson, C., Serradell, L., & Macina, D. (2023). DTaP-IPV-HB-Hib vaccine (Hexaxim): an update 10 years after first licensure. Expert Review of Vaccines, 22(1), 1196–1213. https://doi.org/10.1080/14760584.2023.2280236
2. World Health Organization (2017) The immunological basis for immunization series: module 4: pertussis, update 2017. Available at: https://www.who.int/publications/i/item/the-immunological-basis-for-immunization-series-module-4-pertussis-update-2017
3. Batson A, Glassman A, Federgruen A, et al. The world needs to prepare now to prevent polio resurgence post eradication. BMJ Global Health. 2022;7(12):e011485. doi: https://doi.org/10.1136/bmjgh-2022-011485
4. ReliefWeb. Hexavalent vaccine: less injections and more protection babies. Available at: https://reliefweb.int/report/mauritius/hexavalent-vaccine-less-injections-and-more-protection-babies
5. Olivera, I., Grau, C., Dibarboure, H. et al. Valuing the cost of improving Chilean primary vaccination: a cost minimization analysis of a hexavalent vaccine. BMC Health Serv Res 20, 295 (2020). https://doi.org/10.1186/s12913-020-05115-7
6. Romero M, Góngora D, Caicedo M. Cost-Minimization and Budget Impact Analysis of a Hexavalent Vaccine (Hexaxim®) in the Colombian Expanded Program on Immunization

Value in Health Regional Issues, 2021; 26, 150-159

7. World Bank Data. Available at: https://data.worldbank.org/indicator/SP.DYN.IMRT.IN?locations=DZ
8. World Health Organization. Immunization data: African region. Available at: https://immunizationdata.who.int/dashboard/regions/african-region/DZA
9. World Health Organization.  DTP vaccination coverage. Available at: https://immunizationdata.who.int/global/wiise-detail-page/diphtheria-tetanus-toxoid-and-pertussis-(dtp)-vaccination-coverage?CODE=DZA&ANTIGEN=DTPCV3&YEAR=
10. GPEI – Algeria. Available at https://www.archive.polioeradication.org/where-we-work/algeria/
11. Global Polio Eradication Initiative. GPEI-OPV. polio global eradication initiative . Published 2016. Available at: https://polioeradication.org/polio-today/polio-prevention/the-vaccines/opv/           
12. Practical Implementation Guide for the 2016 National Immunization Schedule in Algeria. Available at: https://cnpm.org.dz/wp-content/uploads/2024/01/Guide_Pratique_de_Mise_en_Oeuvre_du_Nouveau_Calendrier_Natio-1.pdf
13. Laichour A, Kihel M, Aissaoui A, Olivera G. Pharmacoeconomic evaluation of national immunization program realisation in Algeria: cost-minimization analysis of switch from DTwP-Hib + HBV + IPV to an acellular hexavalent (DTaP-HBV-Hib-IPV) vaccine. Poster presented at: ISPOR Europe 2023; November 2023; Copenhagen, Denmark. Value in Health. 2023;26(Suppl 2):S2-EE134.
14. WHO Polio Position Paper 2022. Available at: https://www.who.int/publications/i/item/WHO-WER9725-277-300  
15. Olivera, I., Pérez, C.G., Lazarov, L. et al. Cost minimization analysis of a hexavalent vaccine in Argentina. BMC Health Serv Res 23, 1067 (2023). https://doi.org/10.1186/s12913-023-10038-0
16. Seinfeld J, Rosales ML, Sobrevilla A, López Yescas JG. Economic assessment of incorporating the hexavalent vaccine as part of the National Immunization Program of Peru. BMC Health Serv Res. 2022 May 16;22(1):651. doi: 10.1186/s12913-022-08006-1. PMID: 35570278; PMCID: PMC9109284.
17. Idoko OT, Hampton LM, Mboizi RB, et al. Acceptance of multiple injectable vaccines in a single immunization visit in The Gambia pre and post introduction of inactivated polio vaccine. Vaccine. 2016;34(41):5034-5039. doi: https://doi.org/10.1016/j.vaccine.2016.07.021
18. Hanani Tabana, Dudley L, Knight S, et al. The acceptability of three vaccine injections given to infants during a single clinic visit in South Africa. BMC Public Health. 2016;16(1). doi: https://doi.org/10.1186/s12889-016-3324-2
19. Pelissier JM, Coplan PM, Jackson LA, May JE. The effect of additional shots on the vaccine administration process: results of a time-motion study in 2 settings. Am J Manag Care. 2000 Sep;6(9):1038-44.
20. Al-Bashir L, Ismail A, Aljunid SM. Parents‘ and healthcare professionals’ perception toward the introduction of a new fully liquid hexavalent vaccine in the Malaysian national immunization program: a cross-sectional study instrument development and its application. Front Immunol. 2023;14:1052450.
21. De Coster I, Fournie X, Faure C, Ziani E, Nicolas L, Soubeyrand B, Van Damme P. Assessment of preparation time with fully-liquid versus non-fully liquid paediatric hexavalent vaccines. A time and motion study. Vaccine. 2015;33(32):3976–82.
22. Esteve IC, Fernández PF, Palacios SL, Rodrı́guez MJ, Vino HP, Ortega BR, Nieto Nevot ML, Manch´on GD, L´opez-Belmonte J-L. Health care professionals’ preference for a fully liquid, ready-to-use hexavalent vaccine in Spain. Prev Med Rep. 2021;22:101376.
23. World Health Organization. Immunization data: African region. Available at: https://immunizationdata.who.int/dashboard/regions/african-region
24. Findlater A, Bogoch II. Human Mobility and the Global Spread of Infectious Diseases: A Focus on Air Travel. Trends Parasitol. 2018 Sep;34(9):772-783. doi: 10.1016/j.pt.2018.07.004. Epub 2018 Jul 23. PMID: 30049602; PMCID: PMC7106444.
25. World Health Organization. Immunization data: Mauritius. Available at: https://immunizationdata.who.int/dashboard/regions/african-region/MUS
26. World Health Organization (2025) Fully funded Gavi, the Vaccine Alliance, is a lifeline for child survival, says WHO. Available at: https://www.who.int/news/item/28-03-2025-fully-funded-gavi–the-vaccine-alliance–is-a-lifeline-for-child-survival–says-who

The post An Expert Perspective from Algeria on Hexavalent Vaccine Adoption appeared first on Medika Life.

Behind every budget shift lies a ripple effect, and today, global healthcare is at the receiving end. The result is shrinking access, rising costs, and widening inequities. The impact of geopolitical decisions is rarely confined to borders, it now extends into clinics, hospitals, and communities. What we are witnessing is not just a funding gap, but a structural shift in how the world values health.

The silent reallocation

Over the past years, global defense spending has surged to record levels. According to the Stockholm International Peace Research Institute (SIPRI), global military expenditure rose by 9.4% in real terms to $2718 billion in 2024, the highest global total ever recorded by SIPRI and the 10th year of consecutive increases. The total military expenditure accounted for 2.5% of the global gross domestic product (GDP) in 2024. These figures are expected to climb further following the June 2025 commitment by North Atlantic Treaty Organization (NATO) members to increase defence and security spending to 5% of GDP by 2035, more than twice the earlier 2% benchmark.

Meanwhile, public health budgets are being trimmed or stagnating. The World Health Organization (WHO) has raised serious concerns about the strain on global health systems, pointing to shrinking international aid and ongoing funding shortfalls. In February, Director-General Tedros Adhanom Ghebreyesus highlighted that abrupt and significant reductions in bilateral assistance have severely disrupted healthcare services across multiple countries, describing 2025 as “one of the most challenging years” in the organization’s history.

As per the WHO, an estimated 4.6 billion people worldwide still do not have access to essential health services, while around 2.1 billion experience financial strain due to healthcare expenses. Compounding this challenge, the global health workforce is projected to face a shortfall of 11 million professionals by 2030, with nurses accounting for more than half of this gap. When 4.6 billion people lack access to essential services, this is not only a development failure, but also a global stability risk – translating into fewer vaccinations, delayed disease surveillance, and weakened emergency response systems.

The global policy conversation increasingly treats defence spending as essential security investment. Health spending, however, is still framed as social expenditure rather than strategic infrastructure. This shift is particularly devastating for LMICs, where international aid often fills critical gaps.

Conflict and the collapse of care

Conflict has long been a stress test for health systems, but the consequences today are deeper and more far-reaching. As the Russia–Ukraine war entered its fifth year in 2026, Ukraine’s health system faced unprecedented strain, with attacks on healthcare rising nearly 20% in 2025 alone. Since the full-scale invasion began, the WHO has documented at least 2,881 attacks targeting hospitals, health workers, ambulances, and medical infrastructure, severely disrupting care delivery. Health outcomes have deteriorated sharply, with 59% of people in frontline areas reporting poor health, alongside surging mental health issues, cardiovascular conditions, and widespread lack of access to essential medicines. Intensified attacks in 2025, including a spike in strikes on medical warehouses, have further crippled supply chains.

In the aftermath of the war in Gaza, the region’s health system remains on the brink of collapse despite months of humanitarian efforts and intermittent pauses in fighting. Widespread damage to hospitals, severe shortages of medicines, and a surge in patients continue to overwhelm already fragile services. Many facilities operate at drastically reduced capacity, with critical treatments, including cancer care, meeting only a fraction of demand, while rising cases of skin and respiratory illnesses place further strain on the system. Healthcare workers, operating under extraordinary pressure and with limited resources, are struggling to manage overwhelming caseloads each day amid ongoing supply constraints and infrastructure damage. The crisis extends well beyond acute injuries, as patients with chronic illnesses face dangerous interruptions in care and deteriorating living conditions, turning access to healthcare into a daily struggle for survival.

Meanwhile, after three years of conflict, the Sudan war has evolved into the world’s largest humanitarian and health crisis, with 34 million people in need of aid and 21 million lacking access to basic healthcare. The system is collapsing under the combined weight of widespread disease outbreaks, acute malnutrition affecting over 4 million people, and relentless attacks on healthcare infrastructure—37% of facilities are now non-functional. As infectious diseases surge and funding falls short, Sudan’s health crisis continues to deepen, turning basic healthcare access into a matter of survival for millions.

What distinguishes the current moment is the scale of global interdependence. Earlier crises were largely contained within regions. Today, disruptions are transmitted across borders through tightly integrated supply chains, financing systems, and health workforces. The Ukraine conflict, for instance, has affected global pharmaceutical logistics and energy prices, indirectly increasing healthcare costs across Europe, Asia, and beyond. The result is not just localized breakdowns, but a systemic fragility in global health security.

Conflict as a cost multiplier in health delivery

Healthcare systems are tightly linked to global supply chains, making them highly vulnerable to geopolitical disruptions. When conflicts interrupt trade routes, restrict exports, or trigger sanctions, costs rise almost immediately, across pharmaceuticals, medical devices, and even basic supplies.

Energy shocks add further pressure. Hospitals, being energy-intensive, face higher operating costs as fuel and electricity prices climb. In conflict zones, even critical functions like vaccine cold chains and intensive care become difficult to sustain. The result is a steady increase in healthcare costs, one that is unevenly felt. While high-income countries may cushion the impact through insurance and subsidies, LMICs face a harsher reality, where rising out-of-pocket expenses can push millions into poverty.

At the same time, conflicts are driving up demand for care. Displacement, malnutrition, and infectious disease outbreaks are becoming more frequent, often compounded by climate-related crises. This creates a vicious cycle. Underfunded systems struggle to respond, outcomes worsen, and long-term costs escalate as preventable issues turn into full-blown crises.

Health communicators bridging gaps in a fragmented world

In times of systemic stress, communication becomes a strategic imperative. Health communicators are no longer just interpreters of science. They are interpreters of risk, resilience, and national preparedness. Their role is to translate complex realities into actionable understanding, to advocate for evidence-based decision-making, and to maintain trust in institutions. In today’s environment, narrative gaps are becoming policy gaps. When health is absent from security conversations, it is often absent from security budgets.

In the current context, communicators must:

• Elevate the narrative around health as a security priority, not a secondary concern.
• Highlight the human impact of budget cuts, moving beyond statistics to real stories.
• Counter misinformation proactively, especially in conflict-affected and resource-constrained settings.
• Support policy advocacy, ensuring that health remains central in national and global agendas.

Equally important is the need for communicators to adopt a more systems-oriented approach. This means connecting the dots between geopolitical decisions and health outcomes, helping stakeholders understand that these are deeply interconnected challenges. Investing in health is not just a moral imperative; it is an economic and strategic one. Strong health systems contribute to productivity, stability, and resilience. They are foundational to national security in the broadest sense.

Governments, multilateral organizations, private sector players, and civil society must come together to reassert the importance of health in the global agenda. Innovative financing mechanisms, public-private partnerships, and more efficient use of resources can help bridge funding gaps. But without political will, these solutions will remain insufficient.

If current trajectories persist, the consequences will extend far beyond strained health systems, they will reshape how societies absorb risk, respond to crises, and sustain economic stability. Healthcare cannot remain the residual line item in a world that is becoming more volatile, more interconnected, and more vulnerable. Narratives shape priorities, and priorities shape funding. If health continues to be framed as a cost rather than a cornerstone of resilience, it will keep losing ground to more immediate, visible threats. That framing must change urgently and decisively. Over the next decade, the countries that succeed in protecting population health will not necessarily be those spending the most on healthcare, but those most effectively integrating health into national security thinking. The real question before policymakers and global leaders is whether nations can remain economically stable, politically resilient, or socially secure while treating health as a

The post The Hidden Cost of Global Conflict: Why Health Security Is the First Casualty appeared first on Medika Life.